Tootfinder

Kayak VR: Mirage zeigt, was in der Playstation VR 2 steckt
Mit dem neuen DLC "Yucatšn" schöpft die Kajak-Simulation Kayak VR: Mirage das technische Potenzial der PSVR 2 aus. Wir haben das Paddel geschwungen.

Quantum Simulation of Electron Energy Loss Spectroscopy for Battery Materials
Alexander Kunitsa, Diksha Dhawan, Stepan Fomichev, Juan Miguel Arrazola, Minghao Zhang, Torin F. Stetina
https://arxiv.org/abs/2508.15935

Quantum Simulation of Electron Energy Loss Spectroscopy for Battery Materials
The dynamic structure factor (DSF) is a central quantity for interpreting a vast array of inelastic scattering experiments in chemistry and materials science, but its accurate simulation is a considerable challenge for classical computational methods. In this work, we present a quantum algorithm and an end-to-end simulation framework to compute the DSF, providing a general approach for simulating momentum-resolved spectroscopies. We apply this approach to the simulation of electron energy loss …

A large-scale distributed parallel discrete event simulation engines based on Warped2 for Wargaming simulation
Xiaoning Jia, Ruilin Kong, Guangya Si, Bilong Shen, Zhe Ji
https://arxiv.org/abs/2507.18050

A large-scale distributed parallel discrete event simulation engines based on Warped2 for Wargaming simulation
Rising demand for complex simulations highlights conventional engines'scalability limits, spurring Parallel Discrete Event Simulation (PDES) adoption.Warped2, a PDES engine leveraging Time Warp synchronization with Pending Event Set optimization, delivers strong performance, it struggles with inherent wargaming limitations: inefficient LP resource allocation during synchronization and unaddressed complex entity interaction patterns. To address these challenges, we present an optimized framework…

Beyond Demographics: Enhancing Cultural Value Survey Simulation with Multi-Stage Personality-Driven Cognitive Reasoning
Haijiang Liu, Qiyuan Li, Chao Gao, Yong Cao, Xiangyu Xu, Xun Wu, Daniel Hershcovich, Jinguang Gu
https://arxiv.org/abs/2508.17855

Beyond Demographics: Enhancing Cultural Value Survey Simulation with Multi-Stage Personality-Driven Cognitive Reasoning
Introducing MARK, the Multi-stAge Reasoning frameworK for cultural value survey response simulation, designed to enhance the accuracy, steerability, and interpretability of large language models in this task. The system is inspired by the type dynamics theory in the MBTI psychological framework for personality research. It effectively predicts and utilizes human demographic information for simulation: life-situational stress analysis, group-level personality prediction, and self-weighted cognit…

16WW Eddi PV DHW Diverter Export Margin Analysis (2022-08) - Chosing a more grid-friendly value for the Export Margin based on 3 years' export data. #simulation #gridFriendly #diversion - …

16WW Eddi PV DHW Diverter Export Margin Analysis (2022-08)
Chosing a more grid-friendly value for the Export Margin based on 3 years export data. #simulation #gridFriendly #diversion

A Literature Review on Simulation in Conversational Recommender Systems
Haoran Zhang, Xin Zhao, Jinze Chen, Junpeng Guo
https://arxiv.org/abs/2506.20291 ht…

A Literature Review on Simulation in Conversational Recommender Systems
Conversational Recommender Systems (CRSs) have garnered attention as a novel approach to delivering personalized recommendations through multi-turn dialogues. This review developed a taxonomy framework to systematically categorize relevant publications into four groups: dataset construction, algorithm design, system evaluation, and empirical studies, providing a comprehensive analysis of simulation methods in CRSs research. Our analysis reveals that simulation methods play a key role in tacklin…

Simulation of a closed-loop dc-dc converter using a physics-informed neural network-based model
Marc-Antoine Coulombe, Maxime Berger, Antoine Lesage-Landry
https://arxiv.org/abs/2506.19178

Simulation of a closed-loop dc-dc converter using a physics-informed neural network-based model
The growing reliance on power electronics introduces new challenges requiring detailed time-domain analyses with fast and accurate circuit simulation tools. Currently, commercial time-domain simulation software are mainly relying on physics-based methods to simulate power electronics. Recent work showed that data-driven and physics-informed learning methods can increase simulation speed with limited compromise on accuracy, but many challenges remain before deployment in commercial tools can be …

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation
Chun-Peng Chang, Chen-Yu Wang, Julian Schmidt, Holger Caesar, Alain Pagani
https://arxiv.org/abs/2508.16512

Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation
Recent advancements in video generation have substantially improved visual quality and temporal coherence, making these models increasingly appealing for applications such as autonomous driving, particularly in the context of driving simulation and so-called "world models". In this work, we investigate the effects of existing fine-tuning video generation approaches on structured driving datasets and uncover a potential trade-off: although visual fidelity improves, spatial accuracy in modeling d…

CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference
Luben M. C. Cabezas, Vagner S. Santos, Thiago R. Ramos, Pedro L. C. Rodrigues, Rafael Izbicki
https://arxiv.org/abs/2508.17077

CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference
Current experimental scientists have been increasingly relying on simulation-based inference (SBI) to invert complex non-linear models with intractable likelihoods. However, posterior approximations obtained with SBI are often miscalibrated, causing credible regions to undercover true parameters. We develop $\texttt{CP4SBI}$, a model-agnostic conformal calibration framework that constructs credible sets with local Bayesian coverage. Our two proposed variants, namely local calibration via regres…

RIROS: A Parallel RTL Fault SImulation FRamework with TwO-Dimensional Parallelism and Unified Schedule
Jiaping Tang, Jianan Mu, Zizhen Liu, Ge Yu, Tenghui Hua, Bin Sun, Silin Liu, Jing Ye, Huawei Li
https://arxiv.org/abs/2508.16376

RIROS: A Parallel RTL Fault SImulation FRamework with TwO-Dimensional Parallelism and Unified Schedule
With the rapid development of safety-critical applications such as autonomous driving and embodied intelligence, the functional safety of the corresponding electronic chips becomes more critical. Ensuring chip functional safety requires performing a large number of time-consuming RTL fault simulations during the design phase, significantly increasing the verification cycle. To meet time-to-market demands while ensuring thorough chip verification, parallel acceleration of RTL fault simulation is…

Simulation-Based Inference for Direction Reconstruction of Ultra-High-Energy Cosmic Rays with Radio Arrays
Oscar Macias, Zachary Mason, Matthew Ho, Ars\`ene Ferri\`ere, Aur\'elien Benoit-L\'evy, Mat\'ias Tueros
https://arxiv.org/abs/2508.15991

Simulation-Based Inference for Direction Reconstruction of Ultra-High-Energy Cosmic Rays with Radio Arrays
Ultra-high-energy cosmic-ray (UHECR) observatories require unbiased direction reconstruction to enable multi-messenger astronomy with sparse, nanosecond-scale radio pulses. Explicit likelihood methods often rely on simplified models, which may bias results and understate uncertainties. We introduce a simulation-based inference pipeline that couples a physics-informed graph neural network (GNN) to a normalizing-flow posterior within the \textit{Learning the Universe Implicit Likelihood Inference…

Calculating the power spectrum in stochastic inflation by Monte Carlo simulation and least squares curve fitting
Koichi Miyamoto, Yuichiro Tada
https://arxiv.org/abs/2508.17654 …

Calculating the power spectrum in stochastic inflation by Monte Carlo simulation and least squares curve fitting
The stochastic-$δ\mathcal{N}$ formalism is widely used to study inflation models in which the quantum diffusion of inflatons dominates the background dynamics, leading to interesting phenomena such as the production of primordial black holes. Among numerical approaches to calculate the curvature perturbation spectrum $\mathcal{P}_ζ(k)$ in this formalism, the Monte Carlo simulation-based approach has been proposed as a promising choice, especially in multifield cases. In this approach, we gene…

modelSolver: A Symbolic Model-Driven Solver for Power Network Simulation and Monitoring
Izudin Dzafic, Rabih A. Jabr
https://arxiv.org/abs/2508.17882 https://

modelSolver: A Symbolic Model-Driven Solver for Power Network Simulation and Monitoring
The development of advanced software tools for power system analysis requires extensive programming expertise. Even when using open-source tools, programming skills are essential to modify built-in models. This can be particularly challenging for domain experts who lack coding proficiency. This paper introduces modelSolver, a software solution with a new framework centered around symbolic mathematical modeling. The proposed paradigm facilitates defining models through intuitive mathematical exp…

Universal framework with exponential speedup for the quantum simulation of quantum field theories including QCD
Jad C. Halimeh, Masanori Hanada, Shunji Matsuura
https://arxiv.org/abs/2506.18966

Universal framework with exponential speedup for the quantum simulation of quantum field theories including QCD
We present a quantum simulation framework universally applicable to a wide class of quantum systems, including quantum field theories such as quantum chromodynamics (QCD). Specifically, we generalize an efficient quantum simulation protocol developed for bosonic theories in [Halimeh et al., arXiv:2411.13161] which, when applied to Yang-Mills theory, demonstrated an exponential resource advantage with respect to the truncation level of the bosonic modes, to systems with both bosons and fermions …

A workflow for generating synthetic LiDAR datasets in simulation environments
Abhishek Phadke, Shakib Mahmud Dipto, Pratip Rana
https://arxiv.org/abs/2506.17378

A workflow for generating synthetic LiDAR datasets in simulation environments
This paper presents a simulation workflow for generating synthetic LiDAR datasets to support autonomous vehicle perception, robotics research, and sensor security analysis. Leveraging the CoppeliaSim simulation environment and its Python API, we integrate time-of-flight LiDAR, image sensors, and two dimensional scanners onto a simulated vehicle platform operating within an urban scenario. The workflow automates data capture, storage, and annotation across multiple formats (PCD, PLY, CSV), produ…

Chinese Court Simulation with LLM-Based Agent System
Kaiyuan Zhang, Jiaqi Li, Yueyue Wu, Haitao Li, Cheng Luo, Shaokun Zou, Yujia Zhou, Weihang Su, Qingyao Ai, Yiqun Liu
https://arxiv.org/abs/2508.17322

Chinese Court Simulation with LLM-Based Agent System
Mock trial has long served as an important platform for legal professional training and education. It not only helps students learn about realistic trial procedures, but also provides practical value for case analysis and judgment prediction. Traditional mock trials are difficult to access by the public because they rely on professional tutors and human participants. Fortunately, the rise of large language models (LLMs) provides new opportunities for creating more accessible and scalable court …

A two-prong Approach to the Simulation of DC-RSD: TCAD and Spice
T. Croci, L. Menzio, R. Arcidiacono, M. Arneodo, P. Asenov, N. Cartiglia, M. Ferrero, A. Fondacci, V. Monaco, A. Morozzi, F. Moscatelli, R. Mulargia, E. Robutti, V. Sola, D. Passeri
https://arxiv.org/abs/2508.16324

A two-prong Approach to the Simulation of DC-RSD: TCAD and Spice
The DC-coupled Resistive Silicon Detectors (DCRSD) are the evolution of the AC-coupled RSD (RSD) design, both based on the Low-Gain Avalanche Diode (LGAD) technology. The DC-RSD design concept intends to address a few known issues present in RSDs (e.g., baseline fluctuation, long tail-bipolar signals) while maintaining their advantages (e.g., signal spreading, 100% fill factor). The simulation of DC-RSD presents several unique challenges linked to the complex nature of its design and the large …

Enhancing DSMC simulations of rarefied gas mixtures using a fast-converging and asymptotic-preserving scheme
Liyan Luo, Jianan Zeng, Yanbin Zhang, Wei Li, Qi Li, Lei Wu
https://arxiv.org/abs/2508.17598

Enhancing DSMC simulations of rarefied gas mixtures using a fast-converging and asymptotic-preserving scheme
The numerical simulation of rarefied gas mixture dynamics with disparate masses using the direct simulation Monte Carlo (DSMC) method is slow, primarily because the time step is constrained by that of the lighter species, necessitating an enormous number of evolution steps to reach a steady state. Here, we address this issue by developing a general synthetic iterative scheme, in which the traditional DSMC simulation is intermittently enhanced using a macroscopic synthetic equation. Specifically…

Multivariate Discrete Generalized Pareto Distributions: Theory, Simulation, and Applications to Dry spells
Samira Aka, Marie Kratz, Philippe Naveau
https://arxiv.org/abs/2506.19361

Multivariate Discrete Generalized Pareto Distributions: Theory, Simulation, and Applications to Dry spells
This article extends the multivariate extreme value theory (MEVT) to discrete settings, focusing on the generalized Pareto distribution (GPD) as a foundational tool. The purpose of the study is to enhance the understanding of extreme discrete count data representation, particularly for discrete exceedances over thresholds, defining and using multivariate discrete Pareto distributions (MDGPD). Through theoretical results and illustrative examples, we outline the construction and properties of MD…

The full frequency sweep won't finish before I leave for REcon but I think I've finally got the ARF6 launch dialed in: better than -20 dB return loss from DC to 11.9 GHz, and better than -15.8 dB from DC to 30 GHz.
* Shrink vias from 0.45 pad / 0.25 drill to 0.35 / 0.15
* Use round vias for the signal lines as coupling seemed to be significant vs the square ones I tried to use to simplify meshing
* Keep non-functional via pads
* Single large ground plane cutout o…

A simulation-based training framework for machine-learning applications in ARPES
MengXing Na, Chris Zhou, Sydney K. Y. Dufresne, Matteo Michiardi, Andrea Damascelli
https://arxiv.org/abs/2508.15983

A simulation-based training framework for machine-learning applications in ARPES
In recent years, angle-resolved photoemission spectroscopy (ARPES) has advanced significantly in its ability to probe more observables and simultaneously generate multi-dimensional datasets. These advances present new challenges in data acquisition, processing, and analysis. Machine learning (ML) models can drastically reduce the workload of experimentalists; however, the lack of training data for ML -- and in particular deep learning -- is a significant obstacle. In this work, we introduce an …

OpticalAging: Real-time Presbyopia Simulation for Inclusive Design via Tunable Lenses
Qing Zhang, Zixiong Su, Yoshihito Kondoh, Kazunori Asada, Thad Starner, Kai Kunze, Yuta Itoh, Jun Rekimoto
https://arxiv.org/abs/2506.19307

OpticalAging: Real-time Presbyopia Simulation for Inclusive Design via Tunable Lenses
Presbyopia, a common age-related vision condition affecting most people as they age, often remains inadequately understood by those unaffected. To help bridge the gap between abstract accessibility knowledge and a more grounded appreciation of perceptual challenges, this study presents OpticalAging, an optical see-through simulation approach. Unlike VR-based methods, OpticalAging uses dynamically controlled tunable lenses to simulate the first-person visual perspective of presbyopia's distance-…

Simulation of Flagellated Bacteria Near a Solid Surface: Effects of Flagellar Morphology and Ionic Strength
Baopi Liu, Bowen Jin, Ning An
https://arxiv.org/abs/2506.19271

Simulation of Flagellated Bacteria Near a Solid Surface: Effects of Flagellar Morphology and Ionic Strength
This study systematically investigates the dynamics of the bacterial transition from free-swimming to surface adhesion, a process characterized by both height $h$ and inclination angle $Ψ$. The surface entrapment process is divided into three main stages. Initially, bacteria swim towards the surface at an approach velocity proportional to the motor rotation frequency. Subsequently, during the reorientation stage, the cotangent of the inclination angle decays exponentially with the product of t…

Regular Tree Search for Simulation Optimization
Du-Yi Wang, Guo Liang, Guangwu Liu, Kun Zhang
https://arxiv.org/abs/2506.17696 https://

Regular Tree Search for Simulation Optimization
Tackling simulation optimization problems with non-convex objective functions remains a fundamental challenge in operations research. In this paper, we propose a class of random search algorithms, called Regular Tree Search, which integrates adaptive sampling with recursive partitioning of the search space. The algorithm concentrates simulations on increasingly promising regions by iteratively refining a tree structure. A tree search strategy guides sampling decisions, while partitioning is tri…

MHD simulation of tilt instability during the dynamic FRC magnetic compression process
Yiming Ma, Ping Zhu, Bo Rao, Haolong Li
https://arxiv.org/abs/2506.20443

MHD simulation of tilt instability during the dynamic FRC magnetic compression process
The nonlinear evolution of the tilt instability in a field reversed configuration (FRC) during the dynamic magnetic compression process has been investigated using magnetohydrodynamic (MHD) simulations with the NIMROD code [C. R. Sovinec \textit{et al.}, J. Comput. Phys. \textbf{195}, 355 (2004)]. The tilt mode induces significant deformations in the linear growth phase and results in complete confinement loss of the FRC in the nonlinear phase, with no evidence of dynamic nonlinear stabilizatio…

Towards Real-time Structural Dynamics Simulation with Graph-based Digital Twin Modelling
Jun Zhang, Tong Zhang, Ying Wang
https://arxiv.org/abs/2506.18724 …

Towards Real-time Structural Dynamics Simulation with Graph-based Digital Twin Modelling
Precise and timely simulation of a structure's dynamic behavior is crucial for evaluating its performance and assessing its health status. Traditional numerical methods are often limited by high computational costs and low efficiency, while deep learning approaches offer a promising alternative. However, these data-driven methods still face challenges, such as limited physical interpretability and difficulty in adapting to diverse structural configurations. To address these issues, this study p…

Time-Sensitive Importance Splitting
Gabriel Dengler, Carlos E. Budde, Laura Carnevali, Arnd Hartmanns
https://arxiv.org/abs/2506.19568 https://

Time-Sensitive Importance Splitting
State-of-the-art methods for rare event simulation of non-Markovian models face practical or theoretical limits if observing the event of interest requires prior knowledge or information on the timed behavior of the system. In this paper, we attack both limits by extending importance splitting with a time-sensitive importance function. To this end, we perform backwards reachability search from the target states, considering information about the lower and upper bounds of the active timers in or…

NFL longshot picks, Week 1 2025 odds, props: This parlay could return over $4,000 on a $10 bet

https://www.cbssports.com/nfl/news/nfl-longsho…

Mon retour d'expérience de #Containerlab #simulation

Numerical simulation of transient heat conduction with moving heat source using Physics Informed Neural Networks
Anirudh Kalyan, Sundararajan Natarajan
https://arxiv.org/abs/2506.17726

Numerical simulation of transient heat conduction with moving heat source using Physics Informed Neural Networks
In this paper, the physics informed neural networks (PINNs) is employed for the numerical simulation of heat transfer involving a moving source. To reduce the computational effort, a new training method is proposed that uses a continuous time-stepping through transfer learning. Within this, the time interval is divided into smaller intervals and a single network is initialized. On this single network each time interval is trained with the initial condition for (n+1)th as the solution obtained a…

Simulation-Based Sensitivity Analysis in Optimal Treatment Regimes and Causal Decomposition with Individualized Interventions
Soojin Park, Suyeon Kang, Chioun Lee
https://arxiv.org/abs/2506.19010

Simulation-Based Sensitivity Analysis in Optimal Treatment Regimes and Causal Decomposition with Individualized Interventions
Causal decomposition analysis aims to assess the effect of modifying risk factors on reducing social disparities in outcomes. Recently, this analysis has incorporated individual characteristics when modifying risk factors by utilizing optimal treatment regimes (OTRs). Since the newly defined individualized effects rely on the no omitted confounding assumption, developing sensitivity analyses to account for potential omitted confounding is essential. Moreover, OTRs and individualized effects are…

Fast and Accurate RFIC Performance Prediction via Pin Level Graph Neural Networks and Probabilistic Flow
Anahita Asadi, Leonid Popryho, Inna Partin-Vaisband
https://arxiv.org/abs/2508.16403

Fast and Accurate RFIC Performance Prediction via Pin Level Graph Neural Networks and Probabilistic Flow
Accurately predicting the performance of active radio frequency (RF) circuits is essential for modern wireless systems but remains challenging due to highly nonlinear, layout-sensitive behavior and the high computational cost of traditional simulation tools. Existing machine learning (ML) surrogates often require large datasets to generalize across various topologies or to accurately model skewed and multi-modal performance metrics. In this work, a lightweight, data-efficient, and topology-awar…

Gradient-Based Excitation Filter for Molecular Ground-State Simulation
Runhong He, Qiaozhen Chai, Xin Hong, Ji Guan, Guolong Cui, Shengbin Wang, Shenggang Ying
https://arxiv.org/abs/2506.20398

Gradient-Based Excitation Filter for Molecular Ground-State Simulation
Molecular system simulation is one of the most promising areas for demonstrating practical quantum advantage with near-term quantum computers. The Variational Quantum Eigensolver (VQE) is a leading algorithm for studying molecular energy levels, yet it still faces significant challenges due to excessive circuit depth. This paper introduces a method to efficiently simplify the Unitary Coupled-Cluster with Single and Double Excitations (UCCSD) ansatz in classical computer. We propose to estimate …

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models
Lu Gao, Yongxin Liu, Hongyun Chen, Dahai Liu, Yunpeng Zhang, Jingran Sun
https://arxiv.org/abs/2506.16699

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models
Intelligent Transportation Systems (ITS) are increasingly vulnerable to sophisticated cyberattacks due to their complex, interconnected nature. Ensuring the cybersecurity of these systems is paramount to maintaining road safety and minimizing traffic disruptions. This study presents a novel multi-agent framework leveraging Large Language Models (LLMs) to enhance traffic simulation and cybersecurity testing. The framework automates the creation of traffic scenarios, the design of cyberattack str…

A B-Spline Finite Element Method for Cloth Simulation
Yuqi Meng, Yihao Shi, Kemeng Huang, Ning Guo, Taku Komura, Yin Yang, Minchen Li
https://arxiv.org/abs/2506.18867

A B-Spline Finite Element Method for Cloth Simulation
We present a B-spline finite element method (FEM) for cloth simulation. Building on quadratic B-spline basis functions, our method provides a globally $C^1$-continuous displacement field, enabling consistent and accurate discretization of both membrane and bending energies. This smooth representation effectively mitigates locking artifacts and mesh dependency issues commonly observed with linear FEM. To further improve efficiency, we develop a reduced integration scheme that separately optimize…

A large-scale distributed parallel discrete event simulation engines based on Warped2 for Wargaming simulation
Xiaoning Jia, Ruilin Kong, Guangya Si, Bilong Shen, Zhe Ji
https://arxiv.org/abs/2507.18050

Tight simulation of a distribution using conditional samples
Tomer Adar
https://arxiv.org/abs/2506.18444 https://arxiv.org/pdf/2506.1…

Tight simulation of a distribution using conditional samples
We present an algorithm for simulating a distribution using prefix conditional samples (Adar, Fischer and Levi, 2024), as well as ``prefix-compatible'' conditional models such as the interval model (Cannone, Ron and Servedio, 2015) and the subcube model (CRS15, Bhattacharyya and Chakraborty, 2018). The conditional sample complexity is $O(\log^2 N / \varepsilon^2)$ prefix conditional samples per query, which improves on the previously known $\tilde{O}(\log^3 N / \varepsilon^2)$ (Kumar, Meel and …

Neural networks for the prediction of peel force for skin adhesive interface using FEM simulation
Ashish Masarkar, Rakesh Gupta, Naga Neehar Dingari, Beena Rai
https://arxiv.org/abs/2506.19855

Neural networks for the prediction of peel force for skin adhesive interface using FEM simulation
Studying the peeling behaviour of adhesives on skin is vital for advancing biomedical applications such as medical adhesives and transdermal patches. Traditional methods like experimental testing and finite element method (FEM), though considered gold standards, are resource-intensive, computationally expensive and time-consuming, particularly when analysing a wide material parameter space. In this study, we present a neural network-based approach to predict the minimum peel force (F_min) requi…

Simulation based inference of the ionization history from the 2D 21 cm power spectrum
Nadia Cooper, Carina Norregaard, Romain Meriot, Jonathan R. Pritchard
https://arxiv.org/abs/2508.16329

Simulation based inference of the ionization history from the 2D 21 cm power spectrum
The 21 cm signal contains a wealth of information about the formation of the first stars and the reionization of the intergalactic medium during the Cosmic Dawn (CD) and Epoch of Reionization (EoR). The timing of these important milestones has only roughly been constrained through indirect measurements, such as from the cosmic microwave background (CMB) optical depth, and Lyman-$α$ forest. Therefore, inferring the neutral fraction over cosmic time is a goal of upcoming 21 cm experiments, such …

Volumetric segmentation of muscle compartments using in vivo imaging and architectural validation in human finger flexors
Yang Li
https://arxiv.org/abs/2506.20206

Volumetric segmentation of muscle compartments using in vivo imaging and architectural validation in human finger flexors
Segmenting muscle compartments and measuring their architecture can facilitate movement function assessment, accurate musculoskeletal modeling, and synergy-based electromyogram simulation. Here, we presented a novel method for volumetric segmentation of muscle compartments using in vivo imaging, focusing on the independent compartments for finger control of flexor digitorum superficialis (FDS). Besides, we measured the architectural properties of FDS compartments and validated the segmentation.…

Learning from Scratch: Structurally-masked Transformer for Next Generation Lib-free Simulation
Junlang Huang, Hao Chen, Zhong Guan
https://arxiv.org/abs/2507.17396 https://

Learning from Scratch: Structurally-masked Transformer for Next Generation Lib-free Simulation
This paper proposes a neural framework for power and timing prediction of multi-stage data path, distinguishing itself from traditional lib-based analytical methods dependent on driver characterization and load simplifications. To the best of our knowledge, this is the first language-based, netlist-aware neural network designed explicitly for standard cells. Our approach employs two pre-trained neural models of waveform prediction and delay estimation that directly infer transient waveforms and…

Dynamic Simulation Framework for Disinformation Dissemination and Correction With Social Bots
Boyu Qiao, Kun Li, Wei Zhou, Songlin Hu
https://arxiv.org/abs/2507.16848 https://…

Dynamic Simulation Framework for Disinformation Dissemination and Correction With Social Bots
In the human-bot symbiotic information ecosystem, social bots play key roles in spreading and correcting disinformation. Understanding their influence is essential for risk control and better governance. However, current studies often rely on simplistic user and network modeling, overlook the dynamic behavior of bots, and lack quantitative evaluation of correction strategies. To fill these gaps, we propose MADD, a Multi Agent based framework for Disinformation Dissemination. MADD constructs a m…

ChatChecker: A Framework for Dialogue System Testing and Evaluation Through Non-cooperative User Simulation
Roman Mayr, Michel Schimpf, Thomas Bohn\'e
https://arxiv.org/abs/2507.16792

ChatChecker: A Framework for Dialogue System Testing and Evaluation Through Non-cooperative User Simulation
While modern dialogue systems heavily rely on large language models (LLMs), their implementation often goes beyond pure LLM interaction. Developers integrate multiple LLMs, external tools, and databases. Therefore, assessment of the underlying LLM alone does not suffice, and the dialogue systems must be tested and evaluated as a whole. However, this remains a major challenge. With most previous work focusing on turn-level analysis, less attention has been paid to integrated dialogue-level quali…

Task Allocation of UAVs for Monitoring Missions via Hardware-in-the-Loop Simulation and Experimental Validation
Hamza Chakraa, Fran\c{c}ois Gu\'erin, Edouard Leclercq, Dimitri Lefebvre
https://arxiv.org/abs/2506.20626

Task Allocation of UAVs for Monitoring Missions via Hardware-in-the-Loop Simulation and Experimental Validation
This study addresses the optimisation of task allocation for Unmanned Aerial Vehicles (UAVs) within industrial monitoring missions. The proposed methodology integrates a Genetic Algorithms (GA) with a 2-Opt local search technique to obtain a high-quality solution. Our approach was experimentally validated in an industrial zone to demonstrate its efficacy in real-world scenarios. Also, a Hardware-in-the-loop (HIL) simulator for the UAVs team is introduced. Moreover, insights about the correlatio…

A 3D model simulation of hydrogen chloride photochemistry on Mars: Comparison with satellite data
Benjamin Benne (The University of Edinburgh, School of GeoSciences, UK, Centre for Exoplanet Science, University of Edinburgh, UK), Paul I. Palmer (The University of Edinburgh, School of GeoSciences, UK, Centre for Exoplanet Science, University of Edinburgh, UK), Benjamin M. Taysum (DLR, Germany), Kevin S. Olsen (Department of Physics, University of Oxford, UK, School of Physical Sciences,…

A 3D model simulation of hydrogen chloride photochemistry on Mars: Comparison with satellite data
HCl was detected in the Martian atmosphere by the NOMAD and ACS spectrometers aboard the ExoMars TGO. Photochemical models show that using gas-phase chemistry alone is insufficient to reproduce these data. Recent work has developed a heterogeneous chemical network within a 1D photochemistry model, guided by the seasonal variability in HCl. The aim of this work is to show that incorporating heterogeneous chlorine chemistry into a global 3D model of Martian photochemistry with conventional gas-ph…

M2C: An Open-Source Software for Multiphysics Simulation of Compressible Multi-Material Flows and Fluid-Structure Interactions
Xuning Zhao, Wentao Ma, Shafquat Islam, Aditya Narkhede, Kevin Wang
https://arxiv.org/abs/2508.16387

M2C: An Open-Source Software for Multiphysics Simulation of Compressible Multi-Material Flows and Fluid-Structure Interactions
M2C (Multiphysics Modeling and Computation) is an open-source software for simulating multi-material fluid flows and fluid-structure interactions under extreme conditions, such as high pressures, high temperatures, shock waves, and large interface deformations. It employs a finite volume method to solve the compressible Navier-Stokes equations and supports a wide range of thermodynamic equations of state. M2C incorporates models of laser radiation and absorption, phase transition, and ionizatio…

Understanding the Drag Torque in Common Envelope Evolution
Soumik Bhattacharyya, Luke Chamandy, Eric G. Blackman, Adam Frank, Baowei Liu
https://arxiv.org/abs/2506.19547

Understanding the Drag Torque in Common Envelope Evolution
Common envelope (CE) evolution is largely governed by the drag torque applied on the inspiralling stellar components by the envelope. Previous work has shown that idealized models of the torque based on a single body moving in rectilinear motion through an unperturbed atmosphere can be highly inaccurate. Progress requires new models for the torque that account for binarity. Toward this end we perform a new 3D global hydrodynamic CE simulation with the mass of the companion point particle set eq…

Moderator Simulation for the Compact Positron Source at NLCTA
Ryland Goldman, Sophie Crisp, Spencer Gessner
https://arxiv.org/abs/2508.15975 https://arxiv.…

Moderator Simulation for the Compact Positron Source at NLCTA
Positron moderation is a key part of the production of slow positrons. This paper examines G4beamline simulations of various moderator designs and the addition of a radiofrequency cavity to increase the efficiency of the moderator. A setup consisting of a 100 MeV electron beam incident on a 7 mm tungsten target, a 5 T adiabatic matching device, a 5-cell L-band RF cavity, and a single-crystal tungsten moderator leads to a 39.3x improvement in the number of positrons moderated over a common thin-…

Caregiver-in-the-Loop AI: A Simulation-Based Feasibility Study for Dementia Task Verification
Joy Lai, David Black, Kelly Beaton, Bing Ye, Alex Mihailidis
https://arxiv.org/abs/2508.18267

Caregiver-in-the-Loop AI: A Simulation-Based Feasibility Study for Dementia Task Verification
Caregivers of people living with dementia (PLwD) experience stress when verifying whether tasks are truly completed, even with digital reminder systems. Generative AI, such as GPT-4, may help by automating task verification through follow-up questioning and decision support. This feasibility study evaluates an AI-powered task verification system integrated with digital reminders for PLwD. It examines (1) GPT-4's ability to generate effective follow-up questions, (2) the accuracy of an AI-driv…

Deformation mechanisms of L-PBF-processed Ti-6Al-4V investigated using a combined experimental and simulation approach
Pushkar Prakash Dhekne, Nikhil Prabhu, Matthias B\"onisch, Marc Seefeldt, Martin Diehl, Kim Vanmeensel
https://arxiv.org/abs/2508.16367

Deformation mechanisms of L-PBF-processed Ti-6Al-4V investigated using a combined experimental and simulation approach
Despite the significant application potential of laser powder bed fusion (L-PBF) processed Ti-6Al-4V components, a detailed understanding of their deformation mechanisms remains limited. This study investigates the deformation behavior of the {α^\prime} and α phases in the as-built and heat-treated specimens, respectively, using in-situ high-energy X-ray diffraction (HEXRD) combined with crystal plasticity modeling. Both phases exhibited similar elastic anisotropy, with the highest modulus al…

Simulation of silicon ridge waveguide enhanced two-photon absorption from femtosecond pulses
Cael Warner, Ruoheng Zhang
https://arxiv.org/abs/2506.18098 ht…

Simulation of silicon ridge waveguide enhanced two-photon absorption from femtosecond pulses
Field enhancement of two-photon absorption from a 50-fs pulse on a silicon ridge waveguide is simulated for a varying energy downward propagating 800+/-9.42 nm wavelength plane-wave orthonormal to a waveguide in 2D FDTD using ANSYS Lumerical FDTD. Energy absorbed by the waveguide is enhanced due to mode confinement within the standard 500 nm width, 130 nm height silicon ridge waveguide on 90 nm thick silicon and 3 μm silicon dioxide insulator.

Comparing QCD QED via full simulation versus the RM123 method: U-spin window contribution to $a_\mu^{\mathrm{HVP}}$
A. Altherr, I. Campos, A. Cotellucci, R. Gruber, T. Harris, J. Komijani, F. Margari, M. K. Marinkovic, L. Parato, A. Patella, S. Rosso, N. Tantalo, P. Tavella
https://arxiv.org/abs/2506.19770

Comparing QCD+QED via full simulation versus the RM123 method: U-spin window contribution to $a_μ^{\mathrm{HVP}}$
Electromagnetic corrections to hadronic vacuum polarization contribute significantly to the uncertainty of the Standard Model prediction of the muon anomaly, which poses conceptual and numerical challenges for ab initio lattice determinations. In this study, we compute the non-singlet contribution from intermediate Euclidean current separations in quantum chromo- and electrodynamics (QCD+QED) using C* boundary conditions in two ways: either non-perturbatively by sampling the joint probability d…

Quantifying time in Monte Carlo simulations: application to relaxation processes and AC susceptibilities of magnetic nanoparticles assemblies
A. Morjane, J. -G. Malherbe, J. -J. Alonso, F. Vernay, V. Russier
https://arxiv.org/abs/2508.16343

Quantifying time in Monte Carlo simulations: application to relaxation processes and AC susceptibilities of magnetic nanoparticles assemblies
The study of the response of magnetic nanoparticles (MNP) assemblies to an external alternating magnetic field is of great interest for applications such as hyperthermia. The key quantity here is the complex susceptibility and its behavior in terms of temperature and frequency. From a theoretical point of view it can be obtained by Monte Carlo (MC) simulation with the time quantified Monte Carlo (TQMC) method if a physical time is associated with the MC step. Here we revisit this method by show…

(YouTube, 2024) #NeuraViper cortical visual restoration phosphene simulator https://www.youtube.com/watch?v=hYQEEThyzBY Simulation of cortical brain implant vision for the blind;

Quantum Monte Carlo simulation of light and superlight bipolarons in extended Hubbard-Holstein models on face- and body-centered-cubic lattices
G. D. Adebanjo, J. P. Hague, P. E. Kornilovitch
https://arxiv.org/abs/2507.17398

Quantum Monte Carlo simulation of light and superlight bipolarons in extended Hubbard-Holstein models on face- and body-centered-cubic lattices
Superlight pairing of bipolarons driven by electron-phonon interactions (EPIs) in face-center-cubic (FCC) and body-center-cubic (BCC) lattices is investigated using a continuous-time path-integral quantum Monte Carlo (QMC) algorithm. The EPIs are of the Holstein and extended Holstein types, and a Hubbard interaction is also included. The number of phonons associated with the bipolaron, inverse mass, and radius are calculated and used to construct a phase diagram for bipolaron pairing (identifyi…

Crosslisted article(s) found for astro-ph.IM. https://arxiv.org/list/astro-ph.IM/new
[1/1]:
- CIGaRS I: Combined simulation-based inference from SNae Ia and host photometry
Konstantin Karchev, Roberto Trotta, Raul Jimenez

Mazinkhaos – First Alert 01: Simulation Case
#ambient

Neuro-MSBG: An End-to-End Neural Model for Hearing Loss Simulation
Hui-Guan Yuan, Ryandhimas E. Zezario, Shafique Ahmed, Hsin-Min Wang, Kai-Lung Hua, Yu Tsao
https://arxiv.org/abs/2507.15396

Neuro-MSBG: An End-to-End Neural Model for Hearing Loss Simulation
Hearing loss simulation models are essential for hearing aid deployment. However, existing models have high computational complexity and latency, which limits real-time applications and lack direct integration with speech processing systems. To address these issues, we propose Neuro-MSBG, a lightweight end-to-end model with a personalized audiogram encoder for effective time-frequency modeling. Experiments show that Neuro-MSBG supports parallel inference and retains the intelligibility and perc…

Comparing the dynamics of idealized squall lines between NWP and LES models
Mirjam Tijhuis, Axel Seifert, Alberto de Lozar, Bart J. H. van Stratum, Chiel C. van Heerwaarden
https://arxiv.org/abs/2506.19435

Comparing the dynamics of idealized squall lines between NWP and LES models
Both Numerical Weather Prediction (NWP) models and Large-Eddy Simulation (LES) models are used to simulate convective systems, such as squall lines, but with different purposes. NWP models aim for the most accurate weather forecasts, whereas LES models are typically used to advance our understanding of physical processes. Therefore, these types of models differ in their design. With increasing computer power, the domain sizes and resolutions of these models converge, which raises the question i…

Video Perception Models for 3D Scene Synthesis
Rui Huang, Guangyao Zhai, Zuria Bauer, Marc Pollefeys, Federico Tombari, Leonidas Guibas, Gao Huang, Francis Engelmann
https://arxiv.org/abs/2506.20601

Video Perception Models for 3D Scene Synthesis
Traditionally, 3D scene synthesis requires expert knowledge and significant manual effort. Automating this process could greatly benefit fields such as architectural design, robotics simulation, virtual reality, and gaming. Recent approaches to 3D scene synthesis often rely on the commonsense reasoning of large language models (LLMs) or strong visual priors of modern image generation models. However, current LLMs demonstrate limited 3D spatial reasoning ability, which restricts their ability to…

NOSTRA: A noise-resilient and sparse data framework for trust region based multi objective Bayesian optimization
Maryam Ghasemzadeh, Anton van Beek
https://arxiv.org/abs/2508.16476

NOSTRA: A noise-resilient and sparse data framework for trust region based multi objective Bayesian optimization
Multi-objective Bayesian optimization (MOBO) struggles with sparse (non-space-filling), scarce (limited observations) datasets affected by experimental uncertainty, where identical inputs can yield varying outputs. These challenges are common in physical and simulation experiments (e.g., randomized medical trials and, molecular dynamics simulations) and are therefore incompatible with conventional MOBO methods. As a result, experimental resources are inefficiently allocated, leading to suboptim…

Probing metric fluctuations with the spin of a particle: a quantum simulation with bimodal optical cavities
Jiannis K. Pachos, Patricio Salgado-Rebolledo, Martine Schut
https://arxiv.org/abs/2507.18351

Probing metric fluctuations with the spin of a particle: a quantum simulation with bimodal optical cavities
Exploring potential empirical manifestations of quantum gravity is a challenging pursuit. In this study, we utilise a lattice representation of a (2+1)D massive gravity toy model interacting with Dirac fermions that can support specific spacetime fluctuations. We focus on the evolution of the fermion's spin due to its coupling to spacetime fluctuations. To monitor their dynamics a minimal model is required that comprises two bosonic modes describing spacetime geometry fluctuations coupled with …

CIGaRS I: Combined simulation-based inference from SNae Ia and host photometry
Konstantin Karchev, Roberto Trotta, Raul Jimenez
https://arxiv.org/abs/2508.15899 https://

CIGaRS I: Combined simulation-based inference from SNae Ia and host photometry
Using type Ia supernovae (SNae Ia) as cosmological probes requires empirical corrections, which correlate with their host environment. We present a unified Bayesian hierarchical model designed to infer, from purely photometric observations, the intrinsic dependence of SN Ia brightness on progenitor properties (metallicity & age), the delay-time distribution (DTD) that governs their rate as a function of age, and cosmology, as well as the redshifts of all hosts. The model incorporates physics-ba…

A Complete-Electrode-Model-Based Forward Approach for Transcranial Temporal Interference Stimulation with Linearization: Numerical Simulation Study
Santtu S\"oderholm, Maryam Samavaki, Sampsa Pursiainen
https://arxiv.org/abs/2506.18436

A Complete-Electrode-Model-Based Forward Approach for Transcranial Temporal Interference Stimulation with Linearization: Numerical Simulation Study
Background and Objective: Transcranial temporal interference stimulation (tTIS) is a promising non-invasive brain stimulation technique in which interference between electrical current fields extends the possibilities of electrical brain stimulation. The objective of this study is to develop an efficient mathematical tTIS forward modelling scheme that allows for realistic and adaptable simulation and can be updated accurately when the stimulation frequency is modified in one or more electrodes.…

Can AI Have a Personality? Prompt Engineering for AI Personality Simulation: A Chatbot Case Study in Gender-Affirming Voice Therapy Training
Tailon D. Jackson, Byunggu Yu
https://arxiv.org/abs/2508.18234

Can AI Have a Personality? Prompt Engineering for AI Personality Simulation: A Chatbot Case Study in Gender-Affirming Voice Therapy Training
This thesis investigates whether large language models (LLMs) can be guided to simulate a consistent personality through prompt engineering. The study explores this concept within the context of a chatbot designed for Speech-Language Pathology (SLP) student training, specifically focused on gender-affirming voice therapy. The chatbot, named Monae Jackson, was created to represent a 32-year-old transgender woman and engage in conversations simulating client-therapist interactions. Findings sugge…

Quantification of Sim2Real Gap via Neural Simulation Gap Function
P Sangeerth, Pushpak Jagtap
https://arxiv.org/abs/2506.17675 https://

Quantification of Sim2Real Gap via Neural Simulation Gap Function
In this paper, we introduce the notion of neural simulation gap functions, which formally quantifies the gap between the mathematical model and the model in the high-fidelity simulator, which closely resembles reality. Many times, a controller designed for a mathematical model does not work in reality because of the unmodelled gap between the two systems. With the help of this simulation gap function, one can use existing model-based tools to design controllers for the mathematical system and f…

An Empirical Study of GenAI Adoption in Open-Source Game Development: Tools, Tasks, and Developer Challenges
Xiang Echo Chen, Wenhan Zhu, Guoshuai Albert Shi, Michael W. Godfrey
https://arxiv.org/abs/2507.18029

An Empirical Study of GenAI Adoption in Open-Source Game Development: Tools, Tasks, and Developer Challenges
The growing capabilities of generative AI (GenAI) have begun to reshape how games are designed and developed, offering new tools for content creation, gameplay simulation, and design ideation. While prior research has explored traditional uses of AI in games, such as controlling agents or generating procedural content. There is limited empirical understanding of how GenAI is adopted by developers in real-world contexts, especially within the open-source community. This study aims to explore how…

Evaluating the Impact of Lean and Green Practices on Operational Performance: A Real Data-Driven Simulation Case Study
Farah Altarazi
https://arxiv.org/abs/2506.17354

Evaluating the Impact of Lean and Green Practices on Operational Performance: A Real Data-Driven Simulation Case Study
Global market-driven forces and customer needs are continuously changing. In the past, profitability and efficiency were the primary objectives of most companies. However, in recent decades, sustainable performance has emerged as a new competitive advantage. Companies have been compelled to adopt a concept that combines these evolving global interests with traditional goals resulting in the innovation of the lean and green approach. In this study, a research methodology that includes system a…

Leveraging Correlation Across Test Platforms for Variance-Reduced Metric Estimation
Rachel Luo, Heng Yang, Michael Watson, Apoorva Sharma, Sushant Veer, Edward Schmerling, Marco Pavone
https://arxiv.org/abs/2506.20553

Leveraging Correlation Across Test Platforms for Variance-Reduced Metric Estimation
Learning-based robotic systems demand rigorous validation to assure reliable performance, but extensive real-world testing is often prohibitively expensive, and if conducted may still yield insufficient data for high-confidence guarantees. In this work, we introduce a general estimation framework that leverages paired data across test platforms, e.g., paired simulation and real-world observations, to achieve better estimates of real-world metrics via the method of control variates. By incorpora…

Machine Learning with Privacy for Protected Attributes
Saeed Mahloujifar, Chuan Guo, G. Edward Suh, Kamalika Chaudhuri
https://arxiv.org/abs/2506.19836 htt…

Machine Learning with Privacy for Protected Attributes
Differential privacy (DP) has become the standard for private data analysis. Certain machine learning applications only require privacy protection for specific protected attributes. Using naive variants of differential privacy in such use cases can result in unnecessary degradation of utility. In this work, we refine the definition of DP to create a more general and flexible framework that we call feature differential privacy (FDP). Our definition is simulation-based and allows for both additio…

A modular and extensible library for parameterized terrain generation
Erik Wallin
https://arxiv.org/abs/2506.19751 https://arxiv.org/…

A modular and extensible library for parameterized terrain generation
Simulation-driven development of intelligent machines benefits from artificial terrains with controllable, well-defined characteristics. However, most existing tools for terrain generation focus on artist-driven workflows and visual realism, with limited support for parameterization, reproducibility, or scripting. We present a modular, Python-based library for procedural terrain generation that enables users to construct complex, parameterized terrains by chaining together simple modules. The s…

ChronoLLM: Customizing Language Models for Physics-Based Simulation Code Generation
Jingquan Wang, Andrew Negrut, Harry Zhang, Khailanii Slaton, Shu Wang, Radu Serban, Jinlong Wu, Dan Negrut
https://arxiv.org/abs/2508.13975

ChronoLLM: Customizing Language Models for Physics-Based Simulation Code Generation
This contribution is concerned with the following issue: can pretrained large language models (LLMs) be refined and customized to the point where they become virtual assistants helping experts with the effective use of a simulation tool? In this case study, the ``simulation tool'' considered is PyChrono, an open source multi-physics dynamics engine for multibody systems. We present a framework for refining and customizing both open- and closed-source LLMs to harness the power of AI in generatin…

Estimating the variance-covariance matrix of two-step estimates of latent variable models: A general simulation-based approach
Roberto Di Mari, Jouni Kuha
https://arxiv.org/abs/2507.16324

Estimating the variance-covariance matrix of two-step estimates of latent variable models: A general simulation-based approach
We propose a general procedure for estimating the variance-covariance matrix of two-step estimates of structural parameters in latent variable models. The method is partially simulation-based, in that it includes drawing simulated values of the measurement parameters of the model from their sampling distribution obtained from the first step of two-step estimation, and using them to quantify part of the variability in the parameter estimates from the second step. This is asymptotically equal wit…

Anatomy of the gem5 Simulator: AtomicSimpleCPU, TimingSimpleCPU, O3CPU, and Their Interaction with the Ruby Memory System
Johan S\"oderstr\"om (Uppsala University), Yuan Yao (Uppsala University)
https://arxiv.org/abs/2508.18043

Anatomy of the gem5 Simulator: AtomicSimpleCPU, TimingSimpleCPU, O3CPU, and Their Interaction with the Ruby Memory System
gem5 is a popular modular-based computer system simulator, widely used in computer architecture research and known for its long simulation time and steep learning curve. This report examines its three major CPU models: the AtomicSimpleCPU (AS CPU), the TimingSimpleCPU (TS CPU), the Out-of-order (O3) CPU, and their interactions with the memory subsystem. We provide a detailed anatomical overview of each CPU's function call-chains and present how gem5 partitions its execution time for each simula…

Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation
Tianjun Wei, Huizhong Guo, Yingpeng Du, Zhu Sun, Chen Huang, Dongxia Wang, Jie Zhang
https://arxiv.org/abs/2508.18142

Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation
User simulation is increasingly vital to develop and evaluate recommender systems (RSs). While Large Language Models (LLMs) offer promising avenues to simulate user behavior, they often struggle with the absence of specific domain alignment required for RSs and the efficiency demands of large-scale simulation. A vast yet underutilized resource for enhancing this alignment is the extensive user feedback inherent in RSs. However, directly leveraging such feedback presents two significant challeng…

Coherent and Noncoherent Detection in Dense Arrays: Can We Ignore Mutual Coupling?
Aniol Mart\'i, Luca Sanguinetti, Jaume Riba, Meritxell Lamarca
https://arxiv.org/abs/2506.19470

Coherent and Noncoherent Detection in Dense Arrays: Can We Ignore Mutual Coupling?
This paper investigates the impact of mutual coupling on MIMO systems with densely deployed antennas. Leveraging multiport communication theory, we analyze both coherent and noncoherent detection approaches in a single-user uplink scenario where the receiver ignores mutual coupling effects. Simulation results indicate that while coherent detection is generally more accurate, it is highly sensitive to mismatches in the coupling model, leading to severe performance degradation when antennas are c…

Diffusion of Tracer Particles in Early Growing Biofilms. A Computer Simulation Study
Fabian A. Garcia Daza, Alvaro Rodriguez-Rivas, Fernando Govantes, Alejandro Cuetos
https://arxiv.org/abs/2506.17653

Diffusion of Tracer Particles in Early Growing Biofilms. A Computer Simulation Study
The diffusion of particles in complex media has gained significant interest due to its dual relevance: probing the viscoelastic properties of materials via microrheology and assessing the extent of particle displacement over time. In this work, we explore the early-stage diffusion of a tracer particle within a developing bacterial biofilm using implicit-solvent Brownian dynamics simulations. At these initial stages, bacterial colonies form two-dimensional structures that expand through cell gro…

Inductive Domain Transfer In Misspecified Simulation-Based Inference
Ortal Senouf, Antoine Wehenkel, C\'edric Vincent-Cuaz, Emmanuel Abb\'e, Pascal Frossard
https://arxiv.org/abs/2508.15593

Inductive Domain Transfer In Misspecified Simulation-Based Inference
Simulation-based inference (SBI) is a statistical inference approach for estimating latent parameters of a physical system when the likelihood is intractable but simulations are available. In practice, SBI is often hindered by model misspecification--the mismatch between simulated and real-world observations caused by inherent modeling simplifications. RoPE, a recent SBI approach, addresses this challenge through a two-stage domain transfer process that combines semi-supervised calibration with…

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- DynNPC: Finding More Violations Induced by ADS in Simulation Testing through Dynamic NPC Behavior...
You Lu, Yifan Tian, Dingji Wang, Bihuan Chen, Xin Peng

Deep Intrinsic Coregionalization Multi-Output Gaussian Process Surrogate with Active Learning
Chun-Yi Chang, Chih-Li Sung
https://arxiv.org/abs/2508.16434 https://

Deep Intrinsic Coregionalization Multi-Output Gaussian Process Surrogate with Active Learning
Deep Gaussian Processes (DGPs) are powerful surrogate models known for their flexibility and ability to capture complex functions. However, extending them to multi-output settings remains challenging due to the need for efficient dependency modeling. We propose the Deep Intrinsic Coregionalization Multi-Output Gaussian Process (deepICMGP) surrogate for computer simulation experiments involving multiple outputs, which extends the Intrinsic Coregionalization Model (ICM) by introducing hierarchica…

PHASE: Passive Human Activity Simulation Evaluation
Steven Lamp, Jason D. Hiser, Anh Nguyen-Tuong, Jack W. Davidson
https://arxiv.org/abs/2507.13505 https:…

PHASE: Passive Human Activity Simulation Evaluation
Cybersecurity simulation environments, such as cyber ranges, honeypots, and sandboxes, require realistic human behavior to be effective, yet no quantitative method exists to assess the behavioral fidelity of synthetic user personas. This paper presents PHASE (Passive Human Activity Simulation Evaluation), a machine learning framework that analyzes Zeek connection logs and distinguishes human from non-human activity with over 90\% accuracy. PHASE operates entirely passively, relying on standard …

Exact closure for discrete large-eddy simulation
Syver D{\o}ving Agdestein, Roel Verstappen, Benjamin Sanderse
https://arxiv.org/abs/2507.17051 https://arx…

Exact closure for discrete large-eddy simulation
In this article we propose new discretization-consistent expressions for the sub-filter stress (SFS) tensor in discrete LES, where the filter is induced by the discretization. We introduce a new two-grid filter that allows us to exactly compute the SFS tensor when DNS data is available. This new filter satisfies a "filter-swap" property, such that filtering and finite differencing can be interchanged and the resulting commutator expressions are of structural form (they can be written as the dis…

TOFLUX: A Differentiable Topology Optimization Framework for Multiphysics Fluidic Problems
Rahul Kumar Padhy, Krishnan Suresh, Aaditya Chandrasekhar
https://arxiv.org/abs/2508.17564

TOFLUX: A Differentiable Topology Optimization Framework for Multiphysics Fluidic Problems
Topology Optimization (TO) holds the promise of designing next-generation compact and efficient fluidic devices. However, the inherent complexity of fluid-based TO systems, characterized by multiphysics nonlinear interactions, poses substantial barriers to entry for researchers. Beyond the inherent intricacies of forward simulation models, design optimization is further complicated by the difficulty of computing sensitivities, i.e., gradients. Manual derivation and implementation of sensitivi…

Dual Orthogonal Projections-Based Multiuser Interference Cancellation for mmWave Beamforming in XL-MIMO Systems
Jiazhe Li, Nicol\`o Decarli, Francesco Guidi, Anna Guerra, Alessandro Bazzi, Zhuoming Li
https://arxiv.org/abs/2508.16888

Dual Orthogonal Projections-Based Multiuser Interference Cancellation for mmWave Beamforming in XL-MIMO Systems
This paper investigates multiuser interference (MUI) cancellation for millimeter-wave (mmWave) beamforming in extremely large-scale multiple-input multiple-output (XL-MIMO) communication systems. We propose a linear algorithm, termed iterative dual orthogonal projections (DOP), which alternates between two orthogonal projections: one to eliminate MUI and the other to refine combiners, ensuring a monotonic increase in spectral efficiency. Theoretical analysis and simulation results show that, wi…

GenAI for Automotive Software Development: From Requirements to Wheels
Nenad Petrovic, Fengjunjie Pan, Vahid Zolfaghari, Krzysztof Lebioda, Andre Schamschurko, Alois Knoll
https://arxiv.org/abs/2507.18223

GenAI for Automotive Software Development: From Requirements to Wheels
This paper introduces a GenAI-empowered approach to automated development of automotive software, with emphasis on autonomous and Advanced Driver Assistance Systems (ADAS) capabilities. The process starts with requirements as input, while the main generated outputs are test scenario code for simulation environment, together with implementation of desired ADAS capabilities targeting hardware platform of the vehicle connected to testbench. Moreover, we introduce additional steps for requirements …

An improved lattice Boltzmann method with a novel conservative boundary scheme for viscoelastic fluid flows
Yuan Yu, Siwei Chen, Lei Wang, Hai-zhuan Yuan, Shi Shu
https://arxiv.org/abs/2508.16997

An improved lattice Boltzmann method with a novel conservative boundary scheme for viscoelastic fluid flows
The high Weissenberg number problem has been a persistent challenge in the numerical simulation of viscoelastic fluid flows. This paper presents an improved lattice Boltzmann method for solving viscoelastic flow problems at high Weissenberg numbers. The proposed approach employs two independent two-relaxation-time regularized lattice Boltzmann models to solve the hydrodynamic field and conformation tensor field of viscoelastic fluid flows, respectively. The viscoelastic stress computed from the…

A detailed simulation model for fifth generation district heating and cooling networks with seasonal latent storage evaluated on field data
Manuel Kollmar, Adrian B\"urger, Markus Bohlayer, Angelika Altmann-Dieses, Marco Braun, Moritz Diehl
https://arxiv.org/abs/2506.18528

A detailed simulation model for fifth generation district heating and cooling networks with seasonal latent storage evaluated on field data
Fifth generation district heating and cooling (5GDHC) networks accelerate the use of renewable energies in the heating sector and enable flexible, efficient and future-proof heating and cooling supply via a single network. Due to their low temperature level and high integration of renewables, 5GDHC systems pose new challenges for the modeling of these networks in order to simulate and test operational strategies. A particular feature is the use of uninsulated pipes, which allow energy exchange …

Clifford Accelerated Adaptive QAOA
Th\'eo Lisart-Liebermann, Arcesio Casta\~neda Medina
https://arxiv.org/abs/2508.16443 https://arxiv.org/pdf/2508.164…

Clifford Accelerated Adaptive QAOA
Clifford Circuit Initializaton improves on initial guess of parameters on Parametric Quantum Circuits (PQCs) by leveraging efficient simulation of circuits made out of gates from the Clifford Group. The parameter space is pre-optimized by exploring the Hilbert space in a reduced ensemble of Clifford-expressible points (Clifford Points), providing better initialization. Simultaneously, dynamical circuit reconfiguration algorithms, such as ADAPT-QAOA, improve on QAOA performances by providing a g…

A new data-driven energy-stable Evolve-Filter-Relax model for turbulent flow simulation
Anna Ivagnes, Toby van Gastelen, Syver D{\o}ving Agdestein, Benjamin Sanderse, Giovanni Stabile, Gianluigi Rozza
https://arxiv.org/abs/2507.17423

A new data-driven energy-stable Evolve-Filter-Relax model for turbulent flow simulation
We present a novel approach to define the filter and relax steps in the evolve-filter-relax (EFR) framework for simulating turbulent flows. The EFR main advantages are its ease of implementation and computational efficiency. However, as it only contains two parameters (one for the filter step and one for the relax step) its flexibility is rather limited. In this work, we propose a data-driven approach in which the optimal filter is found based on DNS data in the frequency domain. The optimizati…

Structural DID with ML: Theory, Simulation, and a Roadmap for Applied Research
Yile Yu, Anzhi Xu, Yi Wang
https://arxiv.org/abs/2507.15899 https://

Structural DID with ML: Theory, Simulation, and a Roadmap for Applied Research
Causal inference in observational panel data has become a central concern in economics,policy analysis,and the broader social sciences.To address the core contradiction where traditional difference-in-differences (DID) struggles with high-dimensional confounding variables in observational panel data,while machine learning (ML) lacks causal structure interpretability,this paper proposes an innovative framework called S-DIDML that integrates structural identification with high-dimensional estimat…

Code Difference Guided Fuzzing for FPGA Logic Synthesis Compilers via Bayesian Optimization
Zhihao Xu, Shikai Guo, Guilin Zhao, Peiyu Zou, Siwen Wang, Qian Ma, Hui Li, Furui Zhan
https://arxiv.org/abs/2508.17713

Code Difference Guided Fuzzing for FPGA Logic Synthesis Compilers via Bayesian Optimization
Field Programmable Gate Arrays (FPGAs) play a crucial role in Electronic Design Automation (EDA) applications, which have been widely used in safety-critical environments, including aerospace, chip manufacturing, and medical devices. A critical step in FPGA development is logic synthesis, which enables developers to translate their software designs into hardware net lists, which facilitates the physical implementation of the chip, detailed timing and power analysis, gate-level simulation, test …

Increasing Interaction Fidelity: Training Routines for Biomechanical Models in HCI
Micha{\l} Patryk Miazga, Patrick Ebel
https://arxiv.org/abs/2508.16581 https://

Increasing Interaction Fidelity: Training Routines for Biomechanical Models in HCI
Biomechanical forward simulation holds great potential for HCI, enabling the generation of human-like movements in interactive tasks. However, training biomechanical models with reinforcement learning is challenging, particularly for precise and dexterous movements like those required for touchscreen interactions on mobile devices. Current approaches are limited in their interaction fidelity, require restricting the underlying biomechanical model to reduce complexity, and do not generalize well…

A digital twin of atomic ensemble quantum memories
Elizabeth Robertson, Benjamin Maa{\ss}, Konrad Tschernig, Janik Wolters
https://arxiv.org/abs/2506.20403

A digital twin of atomic ensemble quantum memories
Accurate performance estimation of experimentally demonstrated quantum memories is key to understand the nuances in their deployment in photonic quantum networks. While several software packages allow for accessible quantum simulation, they often do not account for the loss and noise in physical devices. We present a framework for modeling ensemble-based atomic quantum memories using the quantum channel formalism. We provide a Kraus matrix representation of several experimentally implemented st…

Looking for Signs: Reasoning About FOBNNs Using SAT
Hans-J\"org Schurr, Ath\'ena\"is Vaginay
https://arxiv.org/abs/2506.19156 https://…

Looking for Signs: Reasoning About FOBNNs Using SAT
First-Order Boolean Networks with Non-deterministic updates (FOBNN) compute a boolean transition graph representing the absence and presence of species over time. The utility of FOBNNs has been justified by their theoretical soundness with respect to the Euler simulation of the differential equations. However, we lack practical means to work with FOBNNs and an empirical evaluation of their properties. We present a sound and efficient reduction of the first-order FOBNN transition relation to a p…

A High-Order Compact Hermite Difference Method for Double-Diffusive Convection
Jianqing Yang, Jianxian Qiu
https://arxiv.org/abs/2506.19367 https://…

A High-Order Compact Hermite Difference Method for Double-Diffusive Convection
In this paper, a class of high-order compact finite difference Hermite scheme is presented for the simulation of double-diffusive convection. To maintain linear stability, the convective fluxes are split into positive and negative parts, then the compact Hermite difference methods are used to discretize the positive and negative fluxes, respectively. The diffusion fluxes of the governing equations are directly approximated by a high-order finite difference scheme based on the Hermite interpolat…

Towards Deeper Understanding of Natural User Interactions in Virtual Reality Based Assembly Tasks
Ryan Ghamandi, Yahya Hmaiti, Mykola Maslych, Ravi Kiran Kattoju, Joseph J. LaViola Jr
https://arxiv.org/abs/2508.17124

Towards Deeper Understanding of Natural User Interactions in Virtual Reality Based Assembly Tasks
We explore natural user interactions using a virtual reality simulation of a robot arm for assembly tasks. Using a Wizard-of-Oz study, participants completed collaborative LEGO and instructive PCB assembly tasks, with the robot responding under experimenter control. We collected voice, hand tracking, and gaze data from users. Statistical analyses revealed that instructive and collaborative scenarios elicit distinct behaviors and adopted strategies, particularly as tasks progress. Users tended t…

Probing the Physical Reality of Projective Measurements
Leonard Werner Pingen, Mattia Moroder, Sebastian Paeckel
https://arxiv.org/abs/2506.20618 https://

Probing the Physical Reality of Projective Measurements
We propose a protocol to test whether the postulate of a measurement acting as an instantaneous projection onto an eigenstate of the measurement apparatus is compatible with physical reality. This approach is solely based on repeated measurements of local quantities with frequencies that are within reach of analog quantum simulation platforms, for instance Rydberg atom arrays or ultracold gases in optical lattices. Crucially, we also develop a continuous description of a quantum measurement fin…

A novel finite element method for simulating surface plasmon polaritons on complex graphene sheets
Jichun Li, Michael Neunteufel, Li Zhu
https://arxiv.org/abs/2507.17928 https:/…

A novel finite element method for simulating surface plasmon polaritons on complex graphene sheets
Surface plasmon polaritons (SPPs) are generated on the graphene surface, and provide a window into the nano-optical and electrodynamic response of their host material and its dielectric environment. An accurate simulation of SPPs presents several unique challenges, since SPPs often occur at complex interfaces between materials of different dielectric constants and appropriate boundary conditions at the graphene interfaces are crucial. Here we develop a simplified graphene model and propose a ne…

MDR-DeePC: Model-Inspired Distributionally Robust Data-Enabled Predictive Control
Shihao Li, Jiachen Li, Christopher Martin, Soovadeep Bakshi, Dongmei Chen
https://arxiv.org/abs/2506.19744

MDR-DeePC: Model-Inspired Distributionally Robust Data-Enabled Predictive Control
This paper presents a Model-Inspired Distributionally Robust Data-enabled Predictive Control (MDR-DeePC) framework for systems with partially known and uncertain dynamics. The proposed method integrates model-based equality constraints for known dynamics with a Hankel matrix-based representation of unknown dynamics. A distributionally robust optimization problem is formulated to account for parametric uncertainty and stochastic disturbances. Simulation results on a triple-mass-spring-damper sys…

Tighter Error Bounds for the qDRIFT Algorithm
I. J. David, I. Sinayskiy, F. Petruccione
https://arxiv.org/abs/2506.17199 https://arxi…

Tighter Error Bounds for the qDRIFT Algorithm
Randomized algorithms such as qDRIFT provide an efficient framework for quantum simulation by sampling terms from a decomposition of the system's generator. However, existing error bounds for qDRIFT scale quadratically with the norm of the generator, limiting their efficiency for large-scale closed or open quantum system simulation. In this work, we refine the qDRIFT error bound by incorporating Jensen's inequality and a careful treatment of the integral form of the error. This yields an improv…