Tootfinder

Simulation: Neues Teleskop wird bald Millionen Objekte im Sonnensystem entdecken
In wenigen Tagen sollen die ersten Aufnahmen des Vera C. Rubin Observatory präsentiert werden. Nun erhöhen Simulationen die Erwartungen an das Instrument.

Pseudo-Simulation for Autonomous Driving
Wei Cao, Marcel Hallgarten, Tianyu Li, Daniel Dauner, Xunjiang Gu, Caojun Wang, Yakov Miron, Marco Aiello, Hongyang Li, Igor Gilitschenski, Boris Ivanovic, Marco Pavone, Andreas Geiger, Kashyap Chitta
https://arxiv.org/abs/2506.04218

Pseudo-Simulation for Autonomous Driving
Existing evaluation paradigms for Autonomous Vehicles (AVs) face critical limitations. Real-world evaluation is often challenging due to safety concerns and a lack of reproducibility, whereas closed-loop simulation can face insufficient realism or high computational costs. Open-loop evaluation, while being efficient and data-driven, relies on metrics that generally overlook compounding errors. In this paper, we propose pseudo-simulation, a novel paradigm that addresses these limitations. Pseudo…

This https://arxiv.org/abs/2504.04864 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Statistical parametric simulation studies based on real data
Simulation studies are indispensable for evaluating and comparing statistical methods. The most common simulation approach is parametric simulation, where the data-generating mechanism (DGM) corresponds to a predefined parametric model from which observations are drawn. Many statistical simulation studies aim to provide practical recommendations on a method's suitability for a given application; however, parametric simulations in particular are frequently criticized for being too simplistic and…

A Theory-driven and AI-enhanced Simulation Platform for Cultivating Nutrition Literacy
Shan Li, Guozhu Ding
https://arxiv.org/abs/2507.02138 https://

A Theory-driven and AI-enhanced Simulation Platform for Cultivating Nutrition Literacy
This study introduces and evaluates Healthy Choice, an innovative theory-driven and AI-enhanced simulation platform designed to cultivate nutrition literacy through interactive scenario-based learning experiences. We collected feedback from 114 university students with diverse backgrounds who completed simulated product selection scenarios. Quantitative ratings of usefulness and ease of use demonstrated high user satisfaction.

GA-S$^3$: Comprehensive Social Network Simulation with Group Agents
Yunyao Zhang, Zikai Song, Hang Zhou, Wenfeng Ren, Yi-Ping Phoebe Chen, Junqing Yu, Wei Yang
https://arxiv.org/abs/2506.03532

GA-S$^3$: Comprehensive Social Network Simulation with Group Agents
Social network simulation is developed to provide a comprehensive understanding of social networks in the real world, which can be leveraged for a wide range of applications such as group behavior emergence, policy optimization, and business strategy development. However, billions of individuals and their evolving interactions involved in social networks pose challenges in accurately reflecting real-world complexities. In this study, we propose a comprehensive Social Network Simulation System (…

Simulate Any Radar: Attribute-Controllable Radar Simulation via Waveform Parameter Embedding
Weiqing Xiao, Hao Huang, Chonghao Zhong, Yujie Lin, Nan Wang, Xiaoxue Chen, Zhaoxi Chen, Saining Zhang, Shuocheng Yang, Pierre Merriaux, Lei Lei, Hao Zhao
https://arxiv.org/abs/2506.03134

Simulate Any Radar: Attribute-Controllable Radar Simulation via Waveform Parameter Embedding
We present SA-Radar (Simulate Any Radar), a radar simulation approach that enables controllable and efficient generation of radar cubes conditioned on customizable radar attributes. Unlike prior generative or physics-based simulators, SA-Radar integrates both paradigms through a waveform-parameterized attribute embedding. We design ICFAR-Net, a 3D U-Net conditioned on radar attributes encoded via waveform parameters, which captures signal variations induced by different radar configurations. Th…

Neural simulation-based inference of the Higgs trilinear self-coupling via off-shell Higgs production
Aishik Ghosh, Maximilian Griese, Ulrich Haisch, Tae Hyoun Park
https://arxiv.org/abs/2507.02032

Neural simulation-based inference of the Higgs trilinear self-coupling via off-shell Higgs production
One of the forthcoming major challenges in particle physics is the experimental determination of the Higgs trilinear self-coupling. While efforts have largely focused on on-shell double- and single-Higgs production in proton-proton collisions, off-shell Higgs production has also been proposed as a valuable complementary probe. In this article, we design a hybrid neural simulation-based inference (NSBI) approach to construct a likelihood of the Higgs signal incorporating modifications from the S…

This https://arxiv.org/abs/2503.20787 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Advanced simulation paradigm of human behaviour unveils complex financial systemic projection
The high-order complexity of human behaviour is likely the root cause of extreme difficulty in financial market projections. We consider that behavioural simulation can unveil systemic dynamics to support analysis. Simulating diverse human groups must account for the behavioural heterogeneity, especially in finance. To address the fidelity of simulated agents, on the basis of agent-based modeling, we propose a new paradigm of behavioural simulation where each agent is supported and driven by a …

Simulation of MAPS and a MAPS-based Inner Tracker for the Super Tau-Charm Facility
Ruiyang Zhang, Dongwei Xuan, Jiajun Qin, Lei Zhao, Le Xiao, Xiangming Sun, Lailin Xu, Jianbei Liu
https://arxiv.org/abs/2506.03712

Simulation of MAPS and a MAPS-based Inner Tracker for the Super Tau-Charm Facility
Monolithic Active Pixel Sensors (MAPS) are a promising detector candidate for the inner tracker of the Super Tau-Charm Facility (STCF). To evaluate the performance of MAPS and the MAPS-based inner tracker, a dedicated simulation workflow has been developed, offering essential insights for detector design and optimization. The intrinsic characteristics of MAPS, designed using several fabrication processes and pixel geometries, were investigated through a combination of Technology Computer Aide…

Modeling and Simulation of Coupled Biochemical and Two-Phase Compositional Flow in Underground Hydrogen Storage
Elyes Ahmed, Brahim Amaziane, Salaheddine Chabab, St\'ephanie Delage Santacreu, Guillaume Galli\'ero, Olav M{\o}yner, Xavier Raynaud
https://arxiv.org/abs/2506.02582

Modeling and Simulation of Coupled Biochemical and Two-Phase Compositional Flow in Underground Hydrogen Storage
Integrating microbial activity into underground hydrogen storage models is crucial for simulating long-term reservoir behavior. In this work, we present a coupled framework that incorporates bio-geochemical reactions and compositional flow models within the Matlab Reservoir Simulation Toolbox (MRST). Microbial growth and decay are modeled using a double Monod formulation, with populations influenced by hydrogen and carbon dioxide availability. First, a refined Equation of State (EoS) is employe…

Solar Surface Magnetic Field Simulation from 2010 to 2024 and Anomalous Southern Poleward Flux Transport in Cycle 24
Ruihui Wang, Jie Jiang, Yukun Luo
https://arxiv.org/abs/2506.01416

Solar Surface Magnetic Field Simulation from 2010 to 2024 and Anomalous Southern Poleward Flux Transport in Cycle 24
The solar surface magnetic field is fundamental for modeling the coronal magnetic field, studying the solar dynamo, and predicting solar cycle strength. We perform a continuous simulation of the surface magnetic field from 2010 to 2024, covering solar cycle 24 and the ongoing cycle 25, using the surface flux transport model with assimilated observed active regions (ARs) as the source. The simulation reproduces the evolution of the axial dipole strength, polar field reversal timing, and magnetic…

From Spikes to Speech: NeuroVoc -- A Biologically Plausible Vocoder Framework for Auditory Perception and Cochlear Implant Simulation
Jacob de Nobel, Jeroen J. Briaire, Thomas H. W. Baeck, Anna V. Kononova, Johan H. M. Frijns
https://arxiv.org/abs/2506.03959

From Spikes to Speech: NeuroVoc -- A Biologically Plausible Vocoder Framework for Auditory Perception and Cochlear Implant Simulation
We present NeuroVoc, a flexible model-agnostic vocoder framework that reconstructs acoustic waveforms from simulated neural activity patterns using an inverse Fourier transform. The system applies straightforward signal processing to neurogram representations, time-frequency binned outputs from auditory nerve fiber models. Crucially, the model architecture is modular, allowing for easy substitution or modification of the underlying auditory models. This flexibility eliminates the need for speec…

MultiGen: Using Multimodal Generation in Simulation to Learn Multimodal Policies in Real
Renhao Wang, Haoran Geng, Tingle Li, Feishi Wang, Gopala Anumanchipalli, Philipp Wu, Trevor Darrell, Boyi Li, Pieter Abbeel, Jitendra Malik, Alexei A. Efros
https://arxiv.org/abs/2507.02864

MultiGen: Using Multimodal Generation in Simulation to Learn Multimodal Policies in Real
Robots must integrate multiple sensory modalities to act effectively in the real world. Yet, learning such multimodal policies at scale remains challenging. Simulation offers a viable solution, but while vision has benefited from high-fidelity simulators, other modalities (e.g. sound) can be notoriously difficult to simulate. As a result, sim-to-real transfer has succeeded primarily in vision-based tasks, with multimodal transfer still largely unrealized. In this work, we tackle these challenge…

This looks very cool.
'OpenAIRE in collaboration with Area Science Park organizes a hands-on workshop titled “Where LEGO Meets FAIR Data,” designed to introduce the principles of FAIR data through a creative, interactive simulation using LEGO metaphors.'
https://www.

Simulation-Efficient Cosmological Inference with Multi-Fidelity SBI
Leander Thiele, Adrian E. Bayer, Naoya Takeishi
https://arxiv.org/abs/2507.00514 https:…

Simulation-Efficient Cosmological Inference with Multi-Fidelity SBI
The simulation cost for cosmological simulation-based inference can be decreased by combining simulation sets of varying fidelity. We propose an approach to such multi-fidelity inference based on feature matching and knowledge distillation. Our method results in improved posterior quality, particularly for small simulation budgets and difficult inference problems.

'Failure Imminent': When LLMs In a Long-Running Vending Business Simulation Went Berserk
https://slashdot.org/story/25/05/31/2112240/failure-imminent-when-llms-in-a-long-running-vending-business-simulati…

'Failure Imminent': When LLMs In a Long-Running Vending Business Simulation Went Berserk - Slashdot
Long-time Slashdot reader lunchlady55 writes: A pair of researchers investigating the ability of LLMs to coherently operate a simulated vending machine business have recorded hilariously unhinged behavior in many of the current "advanced" LLMs. The LLMs were equipped with several "tools" (code the ...

Playing the long game: why does Putin need a simulation of peace talks with Ukraine? #shorts: https://benborges.xyz/2025/07/01/playing-the-long-game-why.html

Quantifying Student Success with Generative AI: A Monte Carlo Simulation Informed by Systematic Review
Seyma Yaman Kayadibi
https://arxiv.org/abs/2507.01062

Quantifying Student Success with Generative AI: A Monte Carlo Simulation Informed by Systematic Review
The exponential development of generative artificial intelligence (GenAI) technologies like ChatGPT has raised increasing curiosity about their use in higher education, specifically with respect to how students view them, make use of them, and the implications for learning outcomes. This paper employs a hybrid methodological approach involving a systematic literature review and simulation-based modeling to explore student perceptions of GenAI use in the context of higher education. A total of n…

This https://arxiv.org/abs/2409.10875 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Adaptively Coupled Domain Decomposition Method for Multiphase and Multicomponent Porous Media Flows
Numerical simulation of large-scale multiphase and multicomponent flow in porous media is a significant field of interest in the petroleum industry. The fully implicit approach is favored in reservoir simulation due to its numerical stability and relaxed constraints on time-step sizes. However, this method requires solving a large nonlinear system at each time step, making the development of efficient and convergent numerical methods crucial for accelerating the nonlinear solvers. In this paper…

Dyna-Think: Synergizing Reasoning, Acting, and World Model Simulation in AI Agents
Xiao Yu, Baolin Peng, Ruize Xu, Michel Galley, Hao Cheng, Suman Nath, Jianfeng Gao, Zhou Yu
https://arxiv.org/abs/2506.00320

Dyna-Think: Synergizing Reasoning, Acting, and World Model Simulation in AI Agents
Recent progress in reasoning with large language models (LLMs), such as DeepSeek-R1, demonstrates impressive capabilities in domains like mathematics and coding, by exhibiting complex cognitive behaviors such as verification, goal decomposition, and self-reflection. However, it is unclear what behavior is effective and what behavior is missing for long-horizon AI agents tasks. In this work, we propose Dyna-Think, a thinking framework that integrates planning with an internal world model with re…

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning
Siran Chen, Boyu Chen, Chenyun Yu, Yuxiao Luo, Ouyang Yi, Lei Cheng, Chengxiang Zhuo, Zang Li, Yali Wang
https://arxiv.org/abs/2507.02626

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning
Owing to powerful natural language processing and generative capabilities, large language model (LLM) agents have emerged as a promising solution for enhancing recommendation systems via user simulation. However, in the realm of video recommendation, existing studies predominantly resort to prompt-based simulation using frozen LLMs and encounter the intricate challenge of multimodal content understanding. This frequently results in suboptimal item modeling and user preference learning, thereby …

'Failure Imminent': When LLMs In a Long-Running Vending Business Simulation Went Berserk - Slashdot
https://slashdot.org/story/25/05/31/2112240/failure-imminent-when-llms-in-a-long-running-vending-business-simulation-went-berserk

SMT-Sweep: Word-Level Representation Unification for Hardware Verification
Ziyi Yang, Guangyu Hu, Mingkai Miao, Changyuan Yu, Hongce Zhang
https://arxiv.org/abs/2507.02008

SMT-Sweep: Word-Level Representation Unification for Hardware Verification
SAT sweeping has long been a cornerstone technique in logic simplification and equivalence checking at the bit level, leveraging structural hashing, simulation and SAT solving to prune redundant logic. However, with the growing adoption of word-level constructs in hardware verification, such as bit-vector operations, arithmetics and arrays, there lacks a counterpart of SAT sweeping at the word level. In this paper, we introduce SMT-Sweep, a novel extension of SAT sweeping into the word level, g…

fftvis: A Non-Uniform Fast Fourier Transform Based Interferometric Visibility Simulator
Tyler A. Cox, Steven G. Murray, Aaron R. Parsons, Joshua S. Dillon, Kartik Mandar, Zachary E. Martinot, Robert Pascua, Piyanat Kittiwisit, James E. Aguirre
#toXiv_bot_toot

QTurbo: A Robust and Efficient Compiler for Analog Quantum Simulation
Junyu Zhou, Yuhao Liu, Shize Che, Anupam Mitra, Efekan K\"okc\"u, Ermal Rrapaj, Costin Iancu, Gushu Li
https://arxiv.org/abs/2506.22958

QTurbo: A Robust and Efficient Compiler for Analog Quantum Simulation
Analog quantum simulation leverages native hardware dynamics to emulate complex quantum systems with great efficiency by bypassing the quantum circuit abstraction. However, conventional compilation methods for analog simulators are typically labor-intensive, prone to errors, and computationally demanding. This paper introduces QTurbo, a powerful analog quantum simulation compiler designed to significantly enhance compilation efficiency and optimize hardware execution time. By generating precise…

Advancements in Computing and Simulation Techniques for the HIBEAM-NNBAR Experiment
Bernhard Meirose, Jorge Amaral, Alexander Burgman, Matthias Holl, Ernesto Kemp, Adam Kozela, David Milstead, Andr\'e Nepomuceno, Anders Oskarsson, Krzysztof Pysz, Valentina Santoro, Tiago Quirino, Blahoslav Rataj, Gabriel Silva, Samuel Silverstein, Magnus Wolke, Lucas {\AA}strand

Advancements in Computing and Simulation Techniques for the HIBEAM-NNBAR Experiment
The HIBEAM-NNBAR program is a proposed two-stage experiment at the European Spallation Source focusing on searches for baryon number violation processes as well as ultralight dark matter. This paper presents recent advancements in computing and simulation, including machine learning for event selection, fast parametric simulations for detector studies, and detailed modeling of the time projection chamber and readout electronics.

Simulation-Based Inference for Adaptive Experiments
Brian M Cho, Aur\'elien Bibaut, Nathan Kallus
https://arxiv.org/abs/2506.02881 https://

Simulation-Based Inference for Adaptive Experiments
Multi-arm bandit experimental designs are increasingly being adopted over standard randomized trials due to their potential to improve outcomes for study participants, enable faster identification of the best-performing options, and/or enhance the precision of estimating key parameters. Current approaches for inference after adaptive sampling either rely on asymptotic normality under restricted experiment designs or underpowered martingale concentration inequalities that lead to weak power in p…

Molecular dynamics simulation of the effects of neutron irradiation on Caesium Lead Bromide
Zhongming Zhang, Samuel Murphy, Michael Aspinall
https://arxiv.org/abs/2506.00675

Molecular dynamics simulation of the effects of neutron irradiation on Caesium Lead Bromide
With the development of fast neutron reactors and nuclear fusion reactors, it is necessary to find new radiation-hardened high-flux core neutron detectors. The use of perovskite Caesium Lead Bromide (CsPbBr$_3$) for neutron radiation detection is a new research direction. However, at high temperatures, the effects of neutron radiation, especially the Primary Knock-out Atom (PKA) and Displacement Per Atom (DPA), and the defect distribution at the molecular level have not been reported. This stud…

Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources
Roopkatha Banerjee, Prince Modi, Jinal Vyas, Chunduru Sri Abhijit, Tejus Chandrashekar, Harsha Varun Marisetty, Manik Gupta, Yogesh Simmhan
https://arxiv.org/abs/2507.02295

Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources
With the recent improvements in mobile and edge computing and rising concerns of data privacy, Federated Learning(FL) has rapidly gained popularity as a privacy-preserving, distributed machine learning methodology. Several FL frameworks have been built for testing novel FL strategies. However, most focus on validating the learning aspects of FL through pseudo-distributed simulation but not for deploying on real edge hardware in a distributed manner to meaningfully evaluate the federated aspects…

Differentially Private Distribution Release of Gaussian Mixture Models via KL-Divergence Minimization
Hang Liu, Anna Scaglione, Sean Peisert
https://arxiv.org/abs/2506.03467

Differentially Private Distribution Release of Gaussian Mixture Models via KL-Divergence Minimization
Gaussian Mixture Models (GMMs) are widely used statistical models for representing multi-modal data distributions, with numerous applications in data mining, pattern recognition, data simulation, and machine learning. However, recent research has shown that releasing GMM parameters poses significant privacy risks, potentially exposing sensitive information about the underlying data. In this paper, we address the challenge of releasing GMM parameters while ensuring differential privacy (DP) guar…

Simulation and analysis of turbulent flame and its effect on the wall of aero engine combustor
A. Mokhtari, A. Abdallah-elhirtsi, F. Larbi, R. Renane, R. Allouche
https://arxiv.org/abs/2507.01892

Simulation and analysis of turbulent flame and its effect on the wall of aero engine combustor
The main objective of this study is to simulate the behavior of the reactive flow of the turbulent flame in aeronautical combustion chamber of the ALLISON-T56 turboprop, and contribute to the analysis of flame structure and determine for given pressure and temperature of fresh gas the behavior of the thermodynamic parameters of combustion. The numerical approach is based on the resolution of basic equations of turbulent combustion using Ansys-Fluent code where the turbulence model K-e is chosen…

This https://arxiv.org/abs/2412.03318 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Domain-Agnostic Stroke Lesion Segmentation Using Physics-Constrained Synthetic Data
Segmenting stroke lesions in MRI is challenging due to diverse acquisition protocols that limit model generalisability. In this work, we introduce two physics-constrained approaches to generate synthetic quantitative MRI (qMRI) images that improve segmentation robustness across heterogeneous domains. Our first method, $\texttt{qATLAS}$, trains a neural network to estimate qMRI maps from standard MPRAGE images, enabling the simulation of varied MRI sequences with realistic tissue contrasts. The …

Expansion-contraction duality breaking in a Planck-scale sensitive cosmological quantum simulator
S. Mahesh Chandran, Uwe R. Fischer
https://arxiv.org/abs/2506.02719

Expansion-contraction duality breaking in a Planck-scale sensitive cosmological quantum simulator
We propose the experimental simulation of cosmological perturbations governed by a Planck-scale induced Lorentz violating dispersion, aimed at distinguishing between early-universe models with similar power spectra. Employing a novel variant of the scaling approach for the evolution of a Bose-Einstein condensate with both contact and dipolar interactions, we show that scale invariance, and in turn, the duality of the power spectrum is broken at large momenta for an inflating gas, and at small m…

SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL
Jiaheng Hu, Peter Stone, Roberto Mart\'in-Mart\'in
https://arxiv.org/abs/2506.04147

SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL
Building capable household and industrial robots requires mastering the control of versatile, high-degree-of-freedom (DoF) systems such as mobile manipulators. While reinforcement learning (RL) holds promise for autonomously acquiring robot control policies, scaling it to high-DoF embodiments remains challenging. Direct RL in the real world demands both safe exploration and high sample efficiency, which are difficult to achieve in practice. Sim-to-real RL, on the other hand, is often brittle du…

This https://arxiv.org/abs/2307.14984 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

S$^3$: Social-network Simulation System with Large Language Model-Empowered Agents
Social network simulation plays a crucial role in addressing various challenges within social science. It offers extensive applications such as state prediction, phenomena explanation, and policy-making support, among others. In this work, we harness the formidable human-like capabilities exhibited by large language models (LLMs) in sensing, reasoning, and behaving, and utilize these qualities to construct the S$^3$ system (short for $\textbf{S}$ocial network $\textbf{S}$imulation $\textbf{S}$y…

Discrete Element Parameter Calibration of Livestock Salt Based on Particle Scaling
Lulu Nie, Baoqin Wen, Jingbin Li, Shufeng Li, Yali Li, Zhaokun Zhang, Zhiyuan Wang, Zhihao Fan
https://arxiv.org/abs/2506.03786

Discrete Element Parameter Calibration of Livestock Salt Based on Particle Scaling
In order to obtain accurate contact parameters for the discrete element simulation of salt particles used in animal husbandry, the principle of particle contact scaling and dimensional analysis were used for particle scaling. Firstly, the Plackett Burman experiment was used to screen the parameters that significantly affect the angle of repose: salt salt rolling friction coefficient, salt salt recovery coefficient, and salt steel rolling friction coefficient. Considering the influence of other …

Efficient stochastic simulation of gene regulatory networks using hybrid models of transcriptional bursting
Mathilde Gaillard, Ulysse Herbach
https://arxiv.org/abs/2507.01922

Efficient stochastic simulation of gene regulatory networks using hybrid models of transcriptional bursting
Single-cell data reveal the presence of biological stochasticity between cells of identical genome and environment, in particular highlighting the transcriptional bursting phenomenon. To account for this property, gene expression may be modeled as a continuous-time Markov chain where biochemical species are described in a discrete way, leading to Gillespie's stochastic simulation algorithm (SSA) which turns out to be computationally expensive for realistic mRNA and protein copy numbers. Alterna…

Thinning algorithms for the Monte Carlo simulation of kinetic Ising models
V. I. Tokar, H. Dreyss\'e
https://arxiv.org/abs/2505.24414 https://

Thinning algorithms for the Monte Carlo simulation of kinetic Ising models
The thinning method for numerical generation of the nonhomogeneous Poisson process (NHPP) arrival times has been adapted to accelerate Monte Carlo simulations of the kinetic Ising models (KIMs) with the Glauber spin-flip dynamics. The performance of the suggested algorithms has been illustrated by simulation of the decay of metastable states in stationary KIMs and of hysteresis in KIMs in a periodic external field. The thinning has been implemented by means of piecewise constant majorizing func…

Memory Access Vectors: Improving Sampling Fidelity for CPU Performance Simulations
Sriyash Caculo, Mahesh Madhav, Jeff Baxter
https://arxiv.org/abs/2506.02344

Memory Access Vectors: Improving Sampling Fidelity for CPU Performance Simulations
Accurate performance projection of large-scale benchmarks is essential for CPU architects to evaluate and optimize future processor designs. SimPoint sampling, which uses Basic Block Vectors (BBVs), is a widely adopted technique to reduce simulation time by selecting representative program phases. However, BBVs often fail to capture the behavior of applications with extensive array-indirect memory accesses, leading to inaccurate projections. In particular, the 523.xalancbmk_r benchmark exhibits…

Computational adversarial risk analysis for general security games
Jose Manuel Camacho, Roi Naveiro, David Rios Insua
https://arxiv.org/abs/2506.02603 http…

Computational adversarial risk analysis for general security games
This paper provides an efficient computational scheme to handle general security games from an adversarial risk analysis perspective. Two cases in relation to single-stage and multi-stage simultaneous defend-attack games motivate our approach to general setups which uses bi-agent influence diagrams as underlying problem structure and augmented probability simulation as core computational methodology. Theoretical convergence and numerical, modeling, and implementation issues are thoroughly discu…

A modified Levenberg-Marquardt method for estimating the elastic material parameters of polymer waveguides using residuals between autocorrelated frequency responses
Dominik Itner, Dmitrij Dreiling, Hauke Gravenkamp, Bernd Henning, Carolin Birk
https://arxiv.org/abs/2507.01706

A modified Levenberg-Marquardt method for estimating the elastic material parameters of polymer waveguides using residuals between autocorrelated frequency responses
In this contribution, we address the estimation of the frequency-dependent elastic parameters of polymers in the ultrasound range, which is formulated as an inverse problem. This inverse problem is implemented as a nonlinear regression-type optimization problem, in which the simulation signals are fitted to the measurement signals. These signals consist of displacement responses in waveguides, focusing on hollow cylindrical geometries to enhance the simulation efficiency. To accelerate the opti…

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT
Wenqi Zeng, Lu Zhang, Yuan Yao
https://arxiv.org/abs/2507.02734 h…

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT
Long-term biomolecular dynamics are critical for understanding key evolutionary transformations in molecular systems. However, capturing these processes requires extended simulation timescales that often exceed the practical limits of conventional models. To address this, shorter simulations, initialized with diverse perturbations, are commonly used to sample phase space and explore a wide range of behaviors. Recent advances have leveraged language models to infer long-term behavior from short …

Studying mirror acceleration via kinetic simulations of relativistic plasma turbulence
Saikat Das, Siyao Xu, Joonas N\"attil\"a
https://arxiv.org/abs/2506.04212

Studying mirror acceleration via kinetic simulations of relativistic plasma turbulence
Efficient turbulent acceleration of particles is indicated by recent astrophysical observations, but its mechanism is not well understood. Mirror acceleration has recently been proposed as an efficient mechanism for particle energization in turbulence-compressed magnetic fields. We employ a 3D particle-in-cell (PIC) simulation of pair plasma in magnetized and relativistic turbulence to study this new mechanism and its acceleration efficiency. By tracking individual particles, we see that revers…

This https://arxiv.org/abs/2405.08719 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
Driven by steady progress in deep generative modeling, simulation-based inference (SBI) has emerged as the workhorse for inferring the parameters of stochastic simulators. However, recent work has demonstrated that model misspecification can compromise the reliability of SBI, preventing its adoption in important applications where only misspecified simulators are available. This work introduces robust posterior estimation~(RoPE), a framework that overcomes model misspecification with a small re…

IntLevPy: A Python library to classify and model intermittent and L\'evy processes
Shailendra Bhandari, Pedro Lencastre, Sergiy Denysov, Yurii Bystryk, Pedro G. Lind
https://arxiv.org/abs/2506.03729

IntLevPy: A Python library to classify and model intermittent and Lévy processes
IntLevPy provides a comprehensive description of the IntLevPy Package, a Python library designed for simulating and analyzing intermittent and Lévy processes. The package includes functionalities for process simulation, including full parameter estimation and fitting optimization for both families of processes, moment calculation, and classification methods. The classification methodology utilizes adjusted-$R^2$ and a noble performance measure Γ, enabling the distinction between intermittent …

Life Sequence Transformer: Generative Modelling for Counterfactual Simulation
Alberto Cabezas, Carlotta Montorsi
https://arxiv.org/abs/2506.01874 https://

Life Sequence Transformer: Generative Modelling for Counterfactual Simulation
Social sciences rely on counterfactual analysis using surveys and administrative data, generally depending on strong assumptions or the existence of suitable control groups, to evaluate policy interventions and estimate causal effects. We propose a novel approach that leverages the Transformer architecture to simulate counterfactual life trajectories from large-scale administrative records. Our contributions are: the design of a novel encoding method that transforms longitudinal administrative …

Schön nerdiger Longread
https://www.golem.de/news/astrobiologie-woher-kommen-das-leben-das-universum-und-der-ganze-rest-1909-143279.html

Astrobiologie: Woher kommen das Leben, das Universum und der ganze Rest? - Golem.de
Kopf in den Sand! Erst kam der Urknall, dann entstand zufällig Leben – oder es war alles vollkommen anders. Informationen könnten das Wichtigste im Universum sein, und vielleicht leben wir in einer Simulation.

This https://arxiv.org/abs/2505.22415 has been replaced.
initial toot: https://mastoxiv.page/@arX…

ABEL: The Adaptable Beginning-to-End Linac simulation framework
We introduce ABEL, the Adaptable Beginning-to-End Linac simulation framework developed for agile design studies of plasma-based accelerators and colliders. ABEL's modular architecture allows users to simulate particle acceleration across various beamline components. The framework supports specialised codes such as HiPACE++, Wake-T, ELEGANT, GUINEA-PIG, CLICopti and ImpactX, which facilitate precise modelling of complex machine components. Key features include simplified models for addressing tr…

The chemical and spatial variations of the bulge's velocity ellipsoids
Luis M. San Martin Fernandez, Steven Gough-Kelly, Victor P. Debattista, Oscar A. Gonzalez, Ilin Lazar, Alvaro Rojas-Arriagada, Leandro Beraldo e Silva
https://arxiv.org/abs/2506.02876

The chemical and spatial variations of the bulge's velocity ellipsoids
We study the velocity ellipsoids in an $N$-body$+$SPH simulation of a barred galaxy which forms a bar with a BP bulge. We focus on the 2D kinematics, and quantify the velocity ellipses by the anisotropy, $β_{ij}$, the correlation, $ρ_{ij}$, and the vertex deviation, $l_{\rm v}$. We explore the variations in these quantities based on stellar age within the bulge and compare these results with the Milky Way's bulge using data from APOGEE DR16 and {\it Gaia} DR3. We first explore the variation o…

Simulation of Speckle Interferometric Results for Enhanced Measurement and Automated Defect Detection
Jessica Plassmann, Michael Schuth, Georg von Freymann
https://arxiv.org/abs/2507.00732

Simulation of Speckle Interferometric Results for Enhanced Measurement and Automated Defect Detection
Techniques like speckle holography and shearography are rarely applied due to the complexity of instrument setup and lack of automated result analysis, despite their potential. By simulating speckle interferometric outcomes, we seek to address these challenges, enabling more efficient measurement processes and paving the way for automated defect recognition. This research focuses on developing a simulation code for speckle interferometric results derived from finite element analyses. The aim is…

The understanding of the penetration and clusterization of 1-alkanol in bilayer membrane: An open outlook based on atomistic molecular dynamics simulation
Anirban Polley
https://arxiv.org/abs/2505.24152

The understanding of the penetration and clusterization of 1-alkanol in bilayer membrane: An open outlook based on atomistic molecular dynamics simulation
1-alkanols are well known to have anesthetic and penetration properties, though the mode of operation remains enigmatic. We perform extensive atomistic molecular dynamics simulation to study the penetration of 1-alkanols of different chain lengths in the dioleoyl-phosphatidylcholine (DOPC) bilayer model membrane. Our simulations show that the depth of penetration of 1-alkanol increases with chain length, n, and the deuterium order of the DOPC tail increases with the chain length of the acyl-cha…

CyGym: A Simulation-Based Game-Theoretic Analysis Framework for Cybersecurity
Michael Lanier, Yevgeniy Vorobeychik
https://arxiv.org/abs/2506.21688 https:/…

CyGym: A Simulation-Based Game-Theoretic Analysis Framework for Cybersecurity
We introduce a novel cybersecurity encounter simulator between a network defender and an attacker designed to facilitate game-theoretic modeling and analysis while maintaining many significant features of real cyber defense. Our simulator, built within the OpenAI Gym framework, incorporates realistic network topologies, vulnerabilities, exploits (including-zero-days), and defensive mechanisms. Additionally, we provide a formal simulation-based game-theoretic model of cyberdefense using this sim…

Predictions of the LSST Solar System Yield: Near-Earth Objects, Main Belt Asteroids, Jupiter Trojans, and Trans-Neptunian Objects
Jacob A. Kurlander, Pedro H. Bernardinelli, Megan E. Schwamb, Mario Juric, Joseph Murtagh, Colin Orion Chandler, Stephanie R. Merritt, David Nesvorny, David Vokrouhlicky, R. Lynne Jones, Grigori Fedorets, Samuel Cornwall, Matthew J. Holman, Siegfried Eggl, Drew Oldag, Maxine West, Jeremy Kubica, Peter Yoachim, Joachim Moeyens, Kathleen Kiker, Laura E. Buchan…

Predictions of the LSST Solar System Yield: Near-Earth Objects, Main Belt Asteroids, Jupiter Trojans, and Trans-Neptunian Objects
The NSF-DOE Vera C. Rubin Observatory is a new 8m-class survey facility presently being commissioned in Chile, expected to begin the 10yr-long Legacy Survey of Space and Time (LSST) by the end of 2025. Using the purpose-built Sorcha survey simulator (Merritt et al. In Press), and near-final observing cadence, we perform the first high-fidelity simulation of LSST's solar system catalog for key small body populations. We show that the final LSST catalog will deliver over 1.1 billion observations …

This https://arxiv.org/abs/2404.09319 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Impact of curved elements for flows over orography with a Discontinuous Galerkin scheme
We present a quantitative assessment of the impact of high-order mappings on the simulation of flows over complex orography. Curved boundaries were not used in early numerical methods, whereas they are employed to an increasing extent in state of the art computational fluid dynamics codes, in combination with high-order methods, such as the Finite Element Method and the Spectral Element Method. Here we consider a specific Discontinuous Galerkin (DG) method implemented in the framework of the de…

This https://arxiv.org/abs/2502.13454 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Accurate Simulation of the Hubbard Model with Finite Fermionic Projected Entangled Pair States
We demonstrate the use of finite-size fermionic projected entangled pair states, in conjunction with variational Monte Carlo, to perform accurate simulations of the ground-state of the 2D Hubbard model. Using bond dimensions of up to $D=28$, we show that we can surpass state-of-the-art DMRG energies that use up to $m=32000$ SU(2) multiplets on 8-leg ladders. We further apply our methodology to $10\times 16$, $12\times 16$ and $16 \times 16$ lattices at $1/8$ hole doping and observe the dimensio…

This https://arxiv.org/abs/2504.17108 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Computational Physics in the Advanced Lab: Experiment and Simulation of Thermal Diffusion in Metal Rods
We present a simple experiment on thermal diffusion in metal rods that can be used to integrate computation into the advanced lab. An analytical solution exists for the transient heat conduction in an infinite rod with a delta function heat input, but no analytical solution exists for short rods or for long duration heat inputs. Our apparatus is a copper rod with a heater and thermometers attached to the rod. The temperature of the metal rod due to transient heat conduction can be modeled using…

Replaced article(s) found for cs.MA. https://arxiv.org/list/cs.MA/new
[1/1]:
- Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation
Song, Tsern, Wu, Chang, Huang, Chen, Lin, Lin

Ce graphe dit tout...
This graph says it all...
Source (in French but nicely illustrated) https://www.lemonde.fr/les-decodeurs/visuel/2025/07/02/comprendre-le-rechauffement-climatiq…

Comprendre le réchauffement climatique : comment nous avons bouleversé la planète
Deux siècles ont suffi à l’humanité pour dérégler le climat et enclencher une hausse des températures aux effets potentiellement dramatiques. Dans ce format visuel, « Le Monde » retrace cette évolution et les mécanismes à l’œuvre dans ce bouleversement.

This https://arxiv.org/abs/2409.05580 has been replaced.
initial toot: https://mastoxiv.page/@ar…

The design, verification, and applications of Hotspice: a Monte Carlo simulator for artificial spin ice
We present Hotspice, a Monte Carlo simulation software designed to capture the dynamics and equilibrium states of Artificial Spin Ice (ASI) systems with both in-plane (IP) and out-of-plane (OOP) geometries. An Ising-like model is used where each nanomagnet is represented as a macrospin, with switching events driven by thermal fluctuations, magnetostatic interactions, and external fields. To improve simulation accuracy, we explore the impact of several corrections to this model, concerning for e…

A general relativistic hydrodynamic simulation code for studying advective, sub-Keplerian accretion flow onto black holes
Sudip K Garain
https://arxiv.org/abs/2506.01557

A general relativistic hydrodynamic simulation code for studying advective, sub-Keplerian accretion flow onto black holes
In this paper, we describe a general relativistic hydrodynamics simulation code which is developed to simulate advective accretion flow onto black holes. We are particularly interested in the accretion simulations of sub-Keplerian matter in the close vicinity of black holes. Due to the presence of centrifugal barrier, a nearly free-falling sub-Keplerian accretion flow slows down close to a black hole and can even pass through shocks before accelerating again to the black hole. We design our sim…

Constraining Dynamical Dark Energy from Galaxy Clustering with Simulation-Based Priors
Shu-Fan Chen, Mikhail M. Ivanov
https://arxiv.org/abs/2507.00118 htt…

Constraining Dynamical Dark Energy from Galaxy Clustering with Simulation-Based Priors
The effective-field theory based full-shape analysis with simulation-based priors (EFT-SBP) is the novel analysis of galaxy clustering data that allows one to combine merits of perturbation theory and simulation-based modeling in a unified framework. In this paper we use EFT-SBP with the galaxy clustering power spectrum and bispectrum data from BOSS in order to test the recent preference for dynamical dark energy reported by the DESI collaboration. While dynamical dark energy is preferred by th…

GATSim: Urban Mobility Simulation with Generative Agents
Qi Liu, Can Li, Wanjing Ma
https://arxiv.org/abs/2506.23306 https://arxiv.or…

GATSim: Urban Mobility Simulation with Generative Agents
Traditional agent-based urban mobility simulations rely on rigid rule-based systems that fail to capture the complexity, adaptability, and behavioral diversity characteristic of human travel decision-making. Recent advances in large language models and AI agent technology offer opportunities to create agents with reasoning capabilities, persistent memory, and adaptive learning mechanisms. We propose GATSim (Generative-Agent Transport Simulation), a novel framework that leverages these advances …

A Frequentist Simulation-Based Inference Treatment of Sterile Neutrino Global Fits
Joshua Villarreal, Julia Woodward, John Hardin, Janet Conrad
https://arxiv.org/abs/2507.01153

A Frequentist Simulation-Based Inference Treatment of Sterile Neutrino Global Fits
A critical challenge in particle physics is combining results from diverse experimental setups that measure the same physical quantity to enhance precision and statistical power, a process known as a global fit. Global fits of sterile neutrino searches, hunts for additional neutrino oscillation frequencies and amplitudes, present an intriguing case study. In such a scenario, the key assumptions underlying Wilks' theorem, a cornerstone of most classic frequentist analyses, do not hold. The metho…

Fast Simulation of Damage Diffusion Distribution in Scanning Transmission Electron Microscopy
Amir Javadi Rad, Amirafshar Moshtaghpour, Dongdong Chen, Angus I. Kirkland
https://arxiv.org/abs/2507.00294

Fast Simulation of Damage Diffusion Distribution in Scanning Transmission Electron Microscopy
Scanning Transmission Electron Microscopy (STEM) is a critical tool for imaging the properties of materials and biological specimens at atomic scale, yet our understanding of relevant electron beam damage mechanisms is incomplete. Recent studies suggest that certain types of damage can be modelled as a diffusion process. However, numerical simulation of such diffusion processes has remained computationally intensive. This work introduces a high-performance C++ framework for simulating damage di…

Learning with pyCub: A New Simulation and Exercise Framework for Humanoid Robotics
Lukas Rustler, Matej Hoffmann
https://arxiv.org/abs/2506.01756 https://

Learning with pyCub: A New Simulation and Exercise Framework for Humanoid Robotics
We present pyCub, an open-source physics-based simulation of the humanoid robot iCub, along with exercises to teach students the basics of humanoid robotics. Compared to existing iCub similators (iCub SIM, iCub Gazebo), which require C++ code and YARP as middleware, pyCub works without YARP and with Python code. The complete robot with all articulations has been simulated, with two cameras in the eyes and the unique sensitive skin of the iCub comprising 4000 receptors on its body surface. The e…

DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
Kevin Han, Bowen Deng, Amir Barati Farimani, Gerbrand Ceder
https://arxiv.org/abs/2506.02023

DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
Large-scale atomistic simulations are essential to bridge computational materials and chemistry to realistic materials and drug discovery applications. In the past few years, rapid developments of machine learning interatomic potentials (MLIPs) have offered a solution to scale up quantum mechanical calculations. Parallelizing these interatomic potentials across multiple devices poses a challenging, but promising approach to further extending simulation scales to real-world applications. In this…

The complexity of deciding characteristic formulae modulo nested simulation
Luca Aceto, Antonis Achilleos, Aggeliki Chalki, Anna Ingolfsdottir
https://arxiv.org/abs/2505.22277

The complexity of deciding characteristic formulae modulo nested simulation
This paper studies the complexity of determining whether a formula in the modal logics characterizing the nested-simulation semantics is characteristic for some process, which is equivalent to determining whether the formula is satisfiable and prime. The main results are that the problem of determining whether a formula is prime in the modal logic characterizing the 2-nested-simulation preorder is CoNP-complete and is PSPACE-complete in the case of the n-nested-simulation preorder, when n>= 3. …

Realistic quantum network simulation for experimental BBM92 key distribution
Michelle Chalupnik, Brian Doolittle, Suparna Seshadri, Eric G. Brown, Keith Kenemer, Daniel Winton, Daniel Sanchez-Rosales, Matthew Skrzypczyk, Cara Alexander, Eric Ostby, Michael Cubeddu
https://arxiv.org/abs/2505.24851…

Realistic quantum network simulation for experimental BBM92 key distribution
Quantum key distribution (QKD) can provide secure key material between two parties without relying on assumptions about the computational power of an eavesdropper. QKD is performed over quantum links and quantum networks, systems which are resource-intensive to deploy and maintain. To evaluate and optimize performance prior to, during, and after deployment, realistic simulations with attention to physical realism are necessary. Quantum network simulators can simulate a variety of quantum and cl…

Uniform semiclassical observable error bound of Trotterization without the Egorov theorem: a simple algebraic proof
Di Fang, Conrad Qu
https://arxiv.org/abs/2507.02783

Uniform semiclassical observable error bound of Trotterization without the Egorov theorem: a simple algebraic proof
Efficient simulation of the semiclassical Schrödinger equation has garnered significant attention in the numerical analysis community. While controlling the error in the unitary evolution or the wavefunction typically requires the time step size to shrink as the semiclassical parameter $h$ decreases, it has been observed -- and proved for first- and second-order Trotterization schemes -- that the error in certain classes of observables admits a time step size independent of $h$. In this work, …

This https://arxiv.org/abs/2404.01837 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_grqc_…

Rotational mass of anisotropic neutron stars within Rastall gravity
Due to rotation, the mass correction of neutron stars arises and causes the rotational mass to be larger than the static mass. In this work, we extend the formulation of the rotational mass of anisotropic neutron stars within Rastall gravity. We apply numerical simulation on the formulation we obtained. We refer to mass of J0740+6620, GW170817, and GW190814 as the mass constraints of the neutron stars. For the free parameters, we use three values of Rastall's parameter, i.e. $λ=0.00019$, $λ=0…

This https://arxiv.org/abs/2411.15983 has been replaced.
initial toot: https://mastoxiv.page/@a…

Why Grain Growth is Not Curvature Flow
Grain growth in polycrystals is traditionally considered a capillarity-driven process, where grain boundaries (GBs) migrate toward their centers of curvature (i.e., mean curvature flow) with a velocity proportional to the local curvature (including extensions to account for anisotropic GB energy and mobility). Experimental and simulation evidence shows that this simplistic view is untrue. We demonstrate that the failure of the classical mean curvature flow description of grain growth mainly ori…

Numerical simulation of oscillatory magnetic reconnection modulated by solar convective motions
Yifu Wang, Lei Ni, Guanchong Cheng, Jialiang Hu, Yuhao Chen, Abdullah Zafar
https://arxiv.org/abs/2505.24335

Numerical simulation of oscillatory magnetic reconnection modulated by solar convective motions
Oscillatory magnetic reconnection is a periodic magnetic reconnection process, during which the current sheet's orientation and the magnetic connections change periodically. This periodic variation is generally considered to originate from the magnetic reconnection itself rather than from external driving processes. We conduct 2.5-dimensional radiative magnetohydrodynamic simulations to investigate the emergence of a magnetic flux tube from the convection zone into the lower corona, where the e…

A Concurrent Multiscale Framework Coupling Direct Simulation Monte Carlo and Molecular Dynamics
Tim Linke, Dane Sterbentz, Niels Gr{\o}nbech-Jensen, Jean-Pierre Delplanque, Jonathan Belof
https://arxiv.org/abs/2506.01924

A Concurrent Multiscale Framework Coupling Direct Simulation Monte Carlo and Molecular Dynamics
We present a new method to couple the Direct Simulation Monte Carlo (DSMC) algorithm with molecular dynamics (MD). The coupling approach generalizes prior coupling methods using a cell-based decision. The approach is supported by a lifting and restricting operator, which translate length and time scales between DSMC and MD. We verify the framework on basic conservation laws, and demonstrate its usability on a hypersonic flow example. Its accuracy gain is discussed in light of conventional DSMC …

This https://arxiv.org/abs/2501.15011 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Image formation theory of optical coherence tomography with optical aberrations and its application for computational aberration correction
The computational correction of defocus and aberrations in optical coherence tomography (OCT) is a promising approach to achieve high-resolution imaging with deep imaging depth without hardware costs. However, the techniques are not well understood due to the lack of an accurate theoretical model and investigation tools. The image formation theory of optical coherence tomography (OCT) with optical aberrations is formulated. Based on the theory, a numerical simulation method is developed and com…

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback
Rex Chen, Karen Wu, John McCartney, Norman Sadeh, Fei Fang
https://arxiv.org/abs/2505.21349

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback
How can a traffic simulation be designed to faithfully reflect real-world traffic conditions? Past data-driven approaches to traffic simulation in the literature have relied on unrealistic or suboptimal heuristics. They also fail to adequately account for the effects of uncertainty and multimodality in the data on simulation outcomes. In this work, we integrate advances in AI to construct a three-step, end-to-end pipeline for generating a traffic simulation from detector data: computer vision f…

Simulation of images of protoplanetary disks after collision with free-floating planets
Tatiana Demidova, Vitaliy Grigoryev
https://arxiv.org/abs/2506.23795

Simulation of images of protoplanetary disks after collision with free-floating planets
Observational manifestations of disturbances in a protoplanetary disk caused by a collision with a massive planet are studied. It is assumed that the planet moves along a parabolic trajectory that intersects the disk plane near the star. Gas-dynamic simulation is performed using the finite volume method on a long time scale. On its basis, images of the disk observed from the pole and edge-on are constructed in the infrared and submillimeter ranges. A wide range of planet orbit parameters is con…

A simulation study comparing statistical approaches for estimating extreme quantile regression with an application to forecasting of fire risk
Amina El Bernoussi, Mohamed El Arrouchi
https://arxiv.org/abs/2506.23161

A simulation study comparing statistical approaches for estimating extreme quantile regression with an application to forecasting of fire risk
This simulation study compares statistical approaches for estimating extreme quantile regression, with a specific application to fire risk forecasting. A simulation-based framework is designed to evaluate the effectiveness of different methods in capturing extreme dependence structures and accurately predicting extreme quantiles. These approaches are applied to fire occurrence data from the Fez-Meknes region, where a positive relationship is observed between increasing maximum temperatures and …

This https://arxiv.org/abs/2410.00425 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI
Simulation has enabled unprecedented compute-scalable approaches to robot learning. However, many existing simulation frameworks typically support a narrow range of scenes/tasks and lack features critical for scaling generalizable robotics and sim2real. We introduce and open source ManiSkill3, the fastest state-visual GPU parallelized robotics simulator with contact-rich physics targeting generalizable manipulation. ManiSkill3 supports GPU parallelization of many aspects including simulation+re…

Rethinking Dynamic Networks and Heterogeneous Computing with Automatic Parallelization
Ruilong Wu, Xinjiao Li, Yisu Wang, Xinyu Chen, Dirk Kutscher
https://arxiv.org/abs/2506.02787

Rethinking Dynamic Networks and Heterogeneous Computing with Automatic Parallelization
Hybrid parallelism techniques are essential for efficiently training large language models (LLMs). Nevertheless, current automatic parallel planning frameworks often overlook the simultaneous consideration of node heterogeneity and dynamic network topology changes, limiting their effectiveness in practical applications. In this paper, we address these limitations by modeling heterogeneous nodes within dynamically changing network environments and leveraging simulation-based strategies to determ…

The Cosmological analysis of X-ray cluster surveys VII. Bypassing scaling relations with Lagrangian Deep Learning and Simulation-based inference
Nicolas Cerardi, Marguerite Pierre, Fran\c{c}ois Lanusse, Xavier Corap
https://arxiv.org/abs/2507.01820

The Cosmological analysis of X-ray cluster surveys VII. Bypassing scaling relations with Lagrangian Deep Learning and Simulation-based inference
Galaxy clusters, the pinnacle of structure formation in our universe, are a powerful cosmological probe. Several approaches have been proposed to express cluster number counts, but all these methods rely on empirical explicit scaling relations that link observed properties to the total cluster mass. These scaling relations are over-parametrised, inducing some degeneracy with cosmology. Moreover, they do not provide a direct handle on the numerous non-gravitational phenomena that affect the phys…

This https://arxiv.org/abs/2503.04695 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A linearly-implicit energy-momentum preserving scheme for geometrically nonlinear mechanics based on non-canonical Hamiltonian formulations
This work presents a novel formulation and numerical strategy for the simulation of geometrically nonlinear structures. First, a non-canonical Hamiltonian (Poisson) formulation is introduced by including the dynamics of the stress tensor. This framework is developed for von-Kármán nonlinearities in beams and plates, as well as geometrically nonlinear elasticity with Saint-Venant material behavior. In the case of plates, both negligible and non-negligible membrane inertia are considered. For t…

This https://arxiv.org/abs/2505.18851 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Muon Imaging for Illicit Cargo Detection: A Simulation-Based Study
This study evaluates the potential of muon tomography as a non-invasive technique for detecting concealed illicit drugs in cargo, based on detailed simulations performed using the GEANT4 toolkit. A combined analysis of muon scattering and absorption data was employed to enhance material discrimination, with a focus on realistic smuggling scenarios involving cocaine hidden within legitimate cargo. A two-stage inspection protocol is proposed to balance detection speed and resolution. In the first…

This https://arxiv.org/abs/2501.08524 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Deep inference of simulated strong lenses in ground-based surveys
The large number of strong lenses discoverable in future astronomical surveys will likely enhance the value of strong gravitational lensing as a cosmic probe of dark energy and dark matter. However, leveraging the increased statistical power of such large samples will require further development of automated lens modeling techniques. We show that deep learning and simulation-based inference (SBI) methods produce informative and reliable estimates of parameter posteriors for strong lensing syste…

This https://arxiv.org/abs/2503.21368 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

The Jiutian simulations for the CSST extra-galactic surveys
We provide an overview of the Jiutian simulations, a hybrid simulation suite for the China Space Survey Telescope (CSST) extragalactic surveys. It consists of four complementary modules: the primary runs with high resolutions with the fiducial concordance cosmology, the emulator runs exploring the parameter uncertainties around the fiducial cosmology, the reconstruction runs intended for recovering the observed Universe position by position, and the extension runs employing extended cosmologies…

This https://arxiv.org/abs/2503.18616 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

FF-SRL: High Performance GPU-Based Surgical Simulation For Robot Learning
Robotic surgery is a rapidly developing field that can greatly benefit from the automation of surgical tasks. However, training techniques such as Reinforcement Learning (RL) require a high number of task repetitions, which are generally unsafe and impractical to perform on real surgical systems. This stresses the need for simulated surgical environments, which are not only realistic, but also computationally efficient and scalable. We introduce FF-SRL (Fast and Flexible Surgical Reinforcement …

This https://arxiv.org/abs/2505.04227 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Modeling of thin plate flexural vibrations by Partition of Unity Finite Element Method
This paper presents a conforming thin plate bending element based on the Partition of Unity Finite Element Method (PUFEM), for the simulation of steady-state forced vibration. The issue of ensuring the continuity of displacement and slope between elements is addressed by the use of cubic Hermite-type Partition of Unity (PU) functions. With appropriate PU functions, the PUFEM allows the incorporation of the special enrichment functions into the finite elements to better cope with plate oscillati…

When Digital Twins Meet Large Language Models: Realistic, Interactive, and Editable Simulation for Autonomous Driving
Tanmay Vilas Samak, Chinmay Vilas Samak, Bing Li, Venkat Krovi
https://arxiv.org/abs/2507.00319

When Digital Twins Meet Large Language Models: Realistic, Interactive, and Editable Simulation for Autonomous Driving
Simulation frameworks have been key enablers for the development and validation of autonomous driving systems. However, existing methods struggle to comprehensively address the autonomy-oriented requirements of balancing: (i) dynamical fidelity, (ii) photorealistic rendering, (iii) context-relevant scenario orchestration, and (iv) real-time performance. To address these limitations, we present a unified framework for creating and curating high-fidelity digital twins to accelerate advancements i…

Turbulence in Simulated Local Cluster Analogs: one-to-one comparisons between SLOW and XRISM/Hitomi
Frederick Groth, Milena Valentini, Benjamin A. Seidel, Stephan Vladutescu-Zopp, Veronica Biffi, Klaus Dolag, Jenny G. Sorce
https://arxiv.org/abs/2507.02041

Turbulence in Simulated Local Cluster Analogs: one-to-one comparisons between SLOW and XRISM/Hitomi
The XRISM Resolve X-ray spectrometer allows to gain detailed insight into gas motions of the intra cluster medium (ICM) of galaxy clusters. Current simulation studies focus mainly on statistical comparisons, making the comparison to the currently still small number of clusters difficult due to unknown selection effects. This study aims to bridge this gap, using simulated counterparts of Coma, Virgo, and Perseus from the SLOW constrained simulations. These clusters show excellent agreement in th…

Intrinsic Resolution of Organic Scintillators
O. Smirnov
https://arxiv.org/abs/2506.03208 https://arxiv.org/pdf/2506.03208

Intrinsic Resolution of Organic Scintillators
Fluctuations in photon production within scintillators contribute to the overall energy resolution of scintillation detectors. While typically negligible, this contribution - known as intrinsic resolution (IR) - presents significant challenges for both experimental measurement and Monte Carlo simulation. Using the phenomenological model proposed in~[1], we interpreted multiple datasets of IR measurements in organic scintillators. Although our results generally agree with the IR dataset examined…

A2 Copula-Driven Spatial Bayesian Neural Network For Modeling Non-Gaussian Dependence: A Simulation Study
Agnideep Aich, Sameera Hewage, Md Monzur Murshed, Ashit Baran Aich, Amanda Mayeaux, Asim K. Dey, Kumer P. Das, Bruce Wade
https://arxiv.org/abs/2505.24006

A2 Copula-Driven Spatial Bayesian Neural Network For Modeling Non-Gaussian Dependence: A Simulation Study
In this paper, we introduce the A2 Copula Spatial Bayesian Neural Network (A2-SBNN), a predictive spatial model designed to map coordinates to continuous fields while capturing both typical spatial patterns and extreme dependencies. By embedding the dual-tail novel Archimedean copula viz. A2 directly into the network's weight initialization, A2-SBNN naturally models complex spatial relationships, including rare co-movements in the data. The model is trained through a calibration-driven process …

An efficient GPU-accelerated adaptive mesh refinement framework for high-fidelity compressible reactive flows modeling
Yuqi Wang, Yadong Zeng, Ralf Deiterding, Jianhan Liang
https://arxiv.org/abs/2506.02602

An efficient GPU-accelerated adaptive mesh refinement framework for high-fidelity compressible reactive flows modeling
This paper presents a heterogeneous adaptive mesh refinement (AMR) framework for efficient simulation of moderately stiff reactive problems. This framework features an elaborate subcycling-in-time algorithm along with a specialized refluxing algorithm, all unified in a highly parallel codebase. We have also developed a low-storage variant of explicit chemical integrators by optimizing the register usage of GPU, achieving respectively 6x and 3x times speedups as compared to the implicit and stan…

This https://arxiv.org/abs/2407.18820 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

On Godunov-type finite volume methods for seismic wave propagation
The computational complexity of simulating seismic waves demands continual exploration of more efficient numerical methods. While Finite Volume methods are widely acclaimed for tackling general nonlinear hyperbolic (wave) problems, their application in realistic seismic wave simulation remains uncommon, with rare investigations in the literature. Furthermore, seismic wavefields are influenced by sharp subsurface interfaces frequently encountered in realistic models, which could, in principle, b…

A Vehicle-in-the-Loop Simulator with AI-Powered Digital Twins for Testing Automated Driving Controllers
Zengjie Zhang, Giannis Badakis, Michalis Galanis, Adem Bavar\c{s}i, Edwin van Hassel, Mohsen Alirezaei, Sofie Haesaert
https://arxiv.org/abs/2507.02313

A Vehicle-in-the-Loop Simulator with AI-Powered Digital Twins for Testing Automated Driving Controllers
Simulators are useful tools for testing automated driving controllers. Vehicle-in-the-loop (ViL) tests and digital twins (DTs) are widely used simulation technologies to facilitate the smooth deployment of controllers to physical vehicles. However, conventional ViL tests rely on full-size vehicles, requiring large space and high expenses. Also, physical-model-based DT suffers from the reality gap caused by modeling imprecision. This paper develops a comprehensive and practical simulator for tes…

A general polynomial emulator for cosmology via moment projection
Zheng Zhang
https://arxiv.org/abs/2507.02179 https://arxiv.org/pdf/…

A general polynomial emulator for cosmology via moment projection
We present MomentEmu, a general-purpose polynomial emulator for fast and interpretable mappings between theoretical parameters and observational features. The method constructs moment matrices to project simulation data onto polynomial bases, yielding symbolic expressions that approximate the target mapping. Compared to neural-network-based emulators, MomentEmu offers negligible training cost, millisecond-level evaluation, and transparent functional forms. As a demonstration, we develop two emu…

DigiT4TAF -- Bridging Physical and Digital Worlds for Future Transportation Systems
Maximilian Zipfl, Pascal Zwick, Patrick Schulz, Marc Rene Zofka, Albert Schotschneider, Helen Gremmelmaier, Nikolai Polley, Ferdinand M\"utsch, Kevin Simon, Fabian Gottselig, Michael Frey, Sergio Marschall, Akim Stark, Maximilian M\"uller, Marek Wehmer, Mihai Kocsis, Dominic Waldenmayer, Florian Schnepf, Erik Heinrich, Sabrina Pletz, Matthias K\"olle, Karin Langbein-Euchner, Alexander Vie…

DigiT4TAF -- Bridging Physical and Digital Worlds for Future Transportation Systems
In the future, mobility will be strongly shaped by the increasing use of digitalization. Not only will individual road users be highly interconnected, but also the road and associated infrastructure. At that point, a Digital Twin becomes particularly appealing because, unlike a basic simulation, it offers a continuous, bilateral connection linking the real and virtual environments. This paper describes the digital reconstruction used to develop the Digital Twin of the Test Area Autonomous Drivi…

A Chimera domain decomposition method with weak Dirichlet-Robin coupling for finite element simulation of particulate flows
Raphael M\"unster, Otto Mierka, Dmitri Kuzmin, Stefan Turek
https://arxiv.org/abs/2506.22831

A Chimera domain decomposition method with weak Dirichlet-Robin coupling for finite element simulation of particulate flows
We introduce a new multimesh finite element method for direct numerical simulation of incompressible particulate flows. The proposed approach falls into the category of overlapping domain decomposition / Chimera / overset grid meshes. In addition to calculating the velocity and pressure of the fictitious fluid on a fixed background mesh, we solve the incompressible Navier-Stokes equations on body-fitted submeshes that are attached to moving particles. The submesh velocity and pressure are used …

This https://arxiv.org/abs/2505.22642 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control
Reinforcement learning (RL) has driven significant progress in robotics, but its complexity and long training times remain major bottlenecks. In this report, we introduce FastTD3, a simple, fast, and capable RL algorithm that significantly speeds up training for humanoid robots in popular suites such as HumanoidBench, IsaacLab, and MuJoCo Playground. Our recipe is remarkably simple: we train an off-policy TD3 agent with several modifications -- parallel simulation, large-batch updates, a distri…

Sim2Real Diffusion: Learning Cross-Domain Adaptive Representations for Transferable Autonomous Driving
Chinmay Vilas Samak, Tanmay Vilas Samak, Bing Li, Venkat Krovi
https://arxiv.org/abs/2507.00236

Sim2Real Diffusion: Learning Cross-Domain Adaptive Representations for Transferable Autonomous Driving
Simulation-based design, optimization, and validation of autonomous driving algorithms have proven to be crucial for their iterative improvement over the years. Nevertheless, the ultimate measure of effectiveness is their successful transition from simulation to reality (sim2real). However, existing sim2real transfer methods struggle to comprehensively address the autonomy-oriented requirements of balancing: (i) conditioned domain adaptation, (ii) robust performance with limited examples, (iii)…

HumanoidGen: Data Generation for Bimanual Dexterous Manipulation via LLM Reasoning
Zhi Jing, Siyuan Yang, Jicong Ao, Ting Xiao, Yugang Jiang, Chenjia Bai
https://arxiv.org/abs/2507.00833

HumanoidGen: Data Generation for Bimanual Dexterous Manipulation via LLM Reasoning
For robotic manipulation, existing robotics datasets and simulation benchmarks predominantly cater to robot-arm platforms. However, for humanoid robots equipped with dual arms and dexterous hands, simulation tasks and high-quality demonstrations are notably lacking. Bimanual dexterous manipulation is inherently more complex, as it requires coordinated arm movements and hand operations, making autonomous data collection challenging. This paper presents HumanoidGen, an automated task creation and…

This https://arxiv.org/abs/2505.20751 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Interactive OT Gym: A Reinforcement Learning-Based Interactive Optical tweezer (OT)-Driven Microrobotics Simulation Platform
Optical tweezers (OT) offer unparalleled capabilities for micromanipulation with submicron precision in biomedical applications. However, controlling conventional multi-trap OT to achieve cooperative manipulation of multiple complex-shaped microrobots in dynamic environments poses a significant challenge. To address this, we introduce Interactive OT Gym, a reinforcement learning (RL)-based simulation platform designed for OT-driven microrobotics. Our platform supports complex physical field sim…