Tootfinder

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 …

The Indispensable Role of User Simulation in the Pursuit of AGI
Krisztian Balog, ChengXiang Zhai
https://arxiv.org/abs/2509.19456 https://arxiv.org/pdf/250…

The Indispensable Role of User Simulation in the Pursuit of AGI
Progress toward Artificial General Intelligence (AGI) faces significant bottlenecks, particularly in rigorously evaluating complex interactive systems and acquiring the vast interaction data needed for training adaptive agents. This paper posits that user simulation -- creating computational agents that mimic human interaction with AI systems -- is not merely a useful tool, but is a critical catalyst required to overcome these bottlenecks and accelerate AGI development. We argue that realistic …

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…

MechStyle: Augmenting Generative AI with Mechanical Simulation to Create Stylized and Structurally Viable 3D Models
Faraz Faruqi, Amira Abdel-Rahman, Leandra Tejedor, Martin Nisser, Jiaji Li, Vrushank Phadnis, Varun Jampani, Neil Gershenfeld, Megan Hofmann, Stefanie Mueller
https://arxiv.org/abs/2509.20571

MechStyle: Augmenting Generative AI with Mechanical Simulation to Create Stylized and Structurally Viable 3D Models
Recent developments in Generative AI enable creators to stylize 3D models based on text prompts. These methods change the 3D model geometry, which can compromise the model's structural integrity once fabricated. We present MechStyle, a system that enables creators to stylize 3D printable models while preserving their structural integrity. MechStyle accomplishes this by augmenting the Generative AI-based stylization process with feedback from a Finite Element Analysis (FEA) simulation. As the st…

SMC-X: A Distributed Scalable Monte Carlo Simulation Method for Chemically Complex Alloys
Xianglin Liu, Kai Yang, Fanli Zhou, Pengxiang Xu
https://arxiv.org/abs/2509.20949 https…

SMC-X: A Distributed Scalable Monte Carlo Simulation Method for Chemically Complex Alloys
To predict the complex chemical evolution in multicomponent alloys, it is highly desirable to have accurate atomistic simulation methods capable of reaching sufficiently large spatial and temporal scales. In this work, we advance the recently proposed SMC-X method through distributed computation on either GPUs or CPUs, pushing both spatial and temporal scales of atomistic simulation of chemically complex alloys to previously inaccessible scales. This includes a 16-billion-atom HEA system extend…

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…

Characterizing Nanoflare Energy and Frequency through Field Line Analysis
Shanwlee Sow Mondal, Lars K. S. Daldorff, James A. Klimchuk, Craig D. Johnston
https://arxiv.org/abs/2509.20535

Characterizing Nanoflare Energy and Frequency through Field Line Analysis
We present a detailed analysis of a 3D MHD simulation of a subset of the magnetic flux in an active region. The simulation models the generation of nanoflares and response of the plasma to imposed photospheric motions. Our study focuses on characterizing the energy distribution and occurrence frequency of the nanoflares in the simulation that self-consistently heat the corona. This field line based analysis reveals that the nanoflare energy distribution (energy per unit cross sectional area) fo…

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…

Accelerating the Monte Carlo simulation of the Enskog equation for multiscale dense gas flows
Bin Hu, Liyan Luo, Lei Wu
https://arxiv.org/abs/2509.20816 https://

Accelerating the Monte Carlo simulation of the Enskog equation for multiscale dense gas flows
A general synthetic iterative scheme is proposed to solve the Enskog equation within a Monte Carlo framework. The method demonstrates rapid convergence by reducing intermediate Monte Carlo evolution and preserves the asymptotic-preserving property, enabling spatial cell sizes much larger than the mean free path in near-continuum flows. This is realized through mesoscopic-macroscopic two-way coupling: the mesoscopic Monte Carlo simulation provides high-order constitutive relations to close the m…

Bridging Simulation and Silicon: A Study of RISC-V Hardware and FireSim Simulation
Atanu Barai, Kamalavasan Kamalakkannan, Patrick Diehl, Maxim Moraru, Jered Dominguez-Trujillo, Howard Pritchard, Nandakishore Santhi, Farzad Fatollahi-Fard, Galen Shipman
https://arxiv.org/abs/2509.18472

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…

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 …

Study on Locomotive Epidemic Dynamics in a Stochastic Spatio-Temporal Simulation Model on a Multiplex Network
H. M. Shadman Tabib, Jaber Ahmed Deedar, K. M. Ariful Kabir
https://arxiv.org/abs/2509.21017

Study on Locomotive Epidemic Dynamics in a Stochastic Spatio-Temporal Simulation Model on a Multiplex Network
This study presents an integrated approach to understanding epidemic dynamics through a stochastic spatio-temporal simulation model on a multiplex network, blending physical and informational layers. The physical layer maps the geographic movement of individuals, while the information layer tracks the spread of knowledge and health behavior via social interactions. We explore the interplay between physical mobility, information flow, and epidemic outcomes by simulating disease spread within thi…

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 …

A Verified Compiler for Quantum Simulation
Liyi Li, Fenfen An, Federico Zahariev, Zhi Xiang Chong, Amr Sabry, Mark Gordon
https://arxiv.org/abs/2509.18583 https://

«We’re all just projecting the selves that work best in the simulation until we can end the call, take a piss, and finish the egg sandwich we made three Zoom calls ago that’s sitting half-eaten on the kitchen counter.»
https://buttondown.com/monteiro/archive/how-to-come-correct/

The Indispensable Role of User Simulation in the Pursuit of AGI
Krisztian Balog, ChengXiang Zhai
https://arxiv.org/abs/2509.19456 https://arxiv.org/pdf/250…

The Indispensable Role of User Simulation in the Pursuit of AGI
Progress toward Artificial General Intelligence (AGI) faces significant bottlenecks, particularly in rigorously evaluating complex interactive systems and acquiring the vast interaction data needed for training adaptive agents. This paper posits that user simulation -- creating computational agents that mimic human interaction with AI systems -- is not merely a useful tool, but is a critical catalyst required to overcome these bottlenecks and accelerate AGI development. We argue that realistic …

Quantum Simulation and Energy Estimation for Discretized Anharmonic oscillator
Saurav Suman, Bikash K. Behera, Vivek Vyas, Prasanta k. Panigrahi
https://arxiv.org/abs/2509.20907

Quantum Simulation and Energy Estimation for Discretized Anharmonic oscillator
Anharmonic potential quantum system play crucial role in physics as they provide a more realistic description of oscillatory phenomena, which often deviate from the idealized harmonic model. However, simulating such system on classical computers is highly challenging due to nonlinear interactions, large state spaces, and the exponential scaling of memory and computational resources. In this work, quantum simulation is employed to model a quantum anharmonic oscillator (QAHO) using a 3-qubit syst…

FGGS-LiDAR: Ultra-Fast, GPU-Accelerated Simulation from General 3DGS Models to LiDAR
Junzhe Wu, Yufei Jia, Yiyi Yan, Zhixing Chen, Tiao Tan, Zifan Wang, Guangyu Wang
https://arxiv.org/abs/2509.17390

FGGS-LiDAR: Ultra-Fast, GPU-Accelerated Simulation from General 3DGS Models to LiDAR
While 3D Gaussian Splatting (3DGS) has revolutionized photorealistic rendering, its vast ecosystem of assets remains incompatible with high-performance LiDAR simulation, a critical tool for robotics and autonomous driving. We present \textbf{FGGS-LiDAR}, a framework that bridges this gap with a truly plug-and-play approach. Our method converts \textit{any} pretrained 3DGS model into a high-fidelity, watertight mesh without requiring LiDAR-specific supervision or architectural alterations. This …

Scalable Hybrid quantum Monte Carlo simulation of U(1) gauge field coupled to fermions on GPU
Kexin Feng, Chuang Chen, Zi Yang Meng
https://arxiv.org/abs/2508.16298 https://

Scalable Hybrid quantum Monte Carlo simulation of U(1) gauge field coupled to fermions on GPU
We develop a GPU-accelerated hybrid quantum Monte Carlo (QMC) algorithm to solve the fundamental yet difficult problem of $U(1)$ gauge field coupled to fermions, which gives rise to a $U(1)$ Dirac spin liquid state under the description of (2+1)d quantum electrodynamics QED$_3$. The algorithm renders a good acceptance rate and, more importantly, nearly linear space-time volume scaling in computational complexity $O(N_τ V_s)$, where $N_τ$ is the imaginary time dimension and $V_s$ is spatial vo…

Learning Robust Penetration-Testing Policies under Partial Observability: A systematic evaluation
Raphael Simon, Pieter Libin, Wim Mees
https://arxiv.org/abs/2509.20008 https://…

Learning Robust Penetration-Testing Policies under Partial Observability: A systematic evaluation
Penetration testing, the simulation of cyberattacks to identify security vulnerabilities, presents a sequential decision-making problem well-suited for reinforcement learning (RL) automation. Like many applications of RL to real-world problems, partial observability presents a major challenge, as it invalidates the Markov property present in Markov Decision Processes (MDPs). Partially Observable MDPs require history aggregation or belief state estimation to learn successful policies. We investi…

An Analytical and AI-discovered Stable, Accurate, and Generalizable Subgrid-scale Closure for Geophysical Turbulence
Karan Jakhar, Yifei Guan, Pedram Hassanzadeh
https://arxiv.org/abs/2509.20365

An Analytical and AI-discovered Stable, Accurate, and Generalizable Subgrid-scale Closure for Geophysical Turbulence
By combining AI and fluid physics, we discover a closed-form closure for 2D turbulence from small direct numerical simulation (DNS) data. Large-eddy simulation (LES) with this closure is accurate and stable, reproducing DNS statistics including those of extremes. We also show that the new closure could be derived from a 4th-order truncated Taylor expansion. Prior analytical and AI-based work only found the 2nd-order expansion, which led to unstable LES. The additional terms emerge only when int…

NFL longshot picks, 2025 Week 4 odds, player props: This parlay could bring an $8,000 return on $10 bet

https://www.cbssports.com/nfl/news/nfl-lon

Energy transfer from MHD-scale slow-mode waves to kinetic-scale ion acoustic waves
Xiaofei Shi, Xin An, Vassilis Angelopoulos
https://arxiv.org/abs/2509.19763 https://

Energy transfer from MHD-scale slow-mode waves to kinetic-scale ion acoustic waves
Large-amplitude slow-mode waves are commonly observed near Earth's magnetopause. Recent observations show that these waves can occur simultaneously with kinetic-scale ion acoustic waves. The amplitude of the ion acoustic waves is enhanced near the magnetic field peaks of the slow-mode wave, suggesting that the slow-mode waves may drive the generation of ion acoustic waves. To test this hypothesis, we conduct a hybrid simulation using observation-based parameters. The simulation results demonstr…

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…

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…

Neuromorphic Simulation of Drosophila Melanogaster Brain Connectome on Loihi 2
Felix Wang, Bradley H. Theilman, Fred Rothganger, William Severa, Craig M. Vineyard, James B. Aimone
https://arxiv.org/abs/2508.16792

Neuromorphic Simulation of Drosophila Melanogaster Brain Connectome on Loihi 2
We demonstrate the first-ever nontrivial, biologically realistic connectome simulated on neuromorphic computing hardware. Specifically, we implement the whole-brain connectome of the adult Drosophila melanogaster (fruit fly) from the FlyWire Consortium containing 140K neurons and 50M synapses on the Intel Loihi 2 neuromorphic platform. This task is particularly challenging due to the characteristic connectivity structure of biological networks. Unlike artificial neural networks and most abstrac…

A CARLA-based Simulation of Electrically Driven Forklifts
David Claus, Christiane Thielemann, Hans-Georg Stark
https://arxiv.org/abs/2509.15909 https://arx…

A CARLA-based Simulation of Electrically Driven Forklifts
This paper presents the simulation of the operation of an electric forklift fleet within an intralogistics scenario. For this purpose, the open source simulation tool CARLA is used; according to our knowledge this is a novel approach in the context of logistics simulation. First, CARLA is used to generate and visualize a realistic 3D outdoor warehouse scenario, incorporating a number of randomly moving forklifts. In a next step, intralogistics transport tasks, such as pick-and-place, are simula…

Magnetic Dip Found in a Quiescent Prominence Foot via Observation and Simulation
Huadong Chen, Chun Xia, Suli Ma, Yingna Su, Guiping Zhou, Eric Priest, Lyndsay Fletcher, Yuandeng Shen, Weining Tu, Wei Wang, Jun Zhang
https://arxiv.org/abs/2509.19723

Magnetic Dip Found in a Quiescent Prominence Foot via Observation and Simulation
Solar prominences (or filaments) are cooler and denser plasma suspended in the much hotter and rarefied solar corona. When viewed on the solar disc filament barbs or feet protrude laterally from filament spine. When observed at the limb of the Sun, they reach into the chromosphere or even further down. For a long time, the magnetic field orientation of barbs has remained a mystery due to the paradox that the barbs possess vertical fine structures and flows but are likely to be supported in a ho…

Latent class multivariate probit and latent trait models for evaluating test accuracy without a gold standard: A simulation study
Enzo Cerullo, Sean Pinkney, Alex J. Sutton, Tim Lucas, Nicola J. Cooper, Hayley E. Jones
https://arxiv.org/abs/2509.18489

Numerical Simulation for General Relativistic Magnetohydrodynamics in Dynamic Spacetimes
Beibei Li
https://arxiv.org/abs/2508.18221 https://arxiv.org/pdf/2…

Numerical Simulation for General Relativistic Magnetohydrodynamics in Dynamic Spacetimes
We present a novel spectral solver for general relativistic magnetohydrodynamics on dynamical spacetimes. By combining a high order discontinuous spectral method on mapped Chebyshev Fourier grids, our scheme attains exponential convergence. Implemented within a unified BSSN Valencia framework, the code evolves both Einstein and MHD fields self consistently, enabling fully coupled simulations of black hole accretion jet systems. We demonstrate spectral accuracy and entropy stability through conv…

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 …

LLMs don't have physicality, they are (and this is a huge inaccurate stretch) a simulation of how maybe a small part of brains might possibly work in a hugely simplified and abstracted way; an algorithm that is executed on a bunch of graphics cards—the same algorithm could be done manually on paper (with enough time).
Because they're not physical they don't exist.
Therefore there's no epistemology to be had about "do they have sentience or consciousness".

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 …

End-to-End Co-Simulation Testbed for Cybersecurity Research and Development in Intelligent Transportation Systems
Minhaj Uddin Ahmad, Akid Abrar, Sagar Dasgupta, Mizanur Rahman
https://arxiv.org/abs/2509.16489

End-to-End Co-Simulation Testbed for Cybersecurity Research and Development in Intelligent Transportation Systems
Intelligent Transportation Systems (ITS) have been widely deployed across major metropolitan regions worldwide to improve roadway safety, optimize traffic flow, and reduce environmental impacts. These systems integrate advanced sensors, communication networks, and data analytics to enable real-time traffic monitoring, adaptive signal control, and predictive maintenance. However, such integration significantly broadens the ITS attack surface, exposing critical infrastructures to cyber threats th…

Studies on Response Uniformity of RPC and Exploring Oscillation Dip and Valley, Non-Standard Interactions, and Earth's Core using Atmospheric Neutrinos at ICAL-INO detector
Anil Kumar
https://arxiv.org/abs/2509.20094

Studies on Response Uniformity of RPC and Exploring Oscillation Dip and Valley, Non-Standard Interactions, and Earth's Core using Atmospheric Neutrinos at ICAL-INO detector
In this thesis, experimental and physics simulation studies are performed in the context of the proposed 50 kt Iron Calorimeter (ICAL) detector at the India-based Neutrino Observatory (INO). ICAL would detect atmospheric muon neutrinos and antineutrinos separately in the multi-GeV range of energies over a wide range of baselines. In the experimental part of the thesis, the effect of the non-uniformity of the graphite layer on the detector response of the resistive plate chamber is studied. In t…

AI-enhanced Quantum Simulation of Schwinger Model
Ao-Ning Wang, Min-Quan He, Z. D. Wang
https://arxiv.org/abs/2509.20173 https://arxiv.org/pdf/2509.20173…

AI-enhanced Quantum Simulation of Schwinger Model
The Schwinger Model from Quantum Electrodynamics (QED) has long served as a valuable simplified model for exploring key physical phenomena in Quantum Chromodynamics (QCD)-a field rich with fundamental insights but is substantially more complex. While the phase diagram of the Schwinger Model bears extraordinary significance and remains challenging to investigate, recent progress on the model mainly focuses on detailed case studies. Here, we propose a model that we refer as the Neural Network Fac…

On Asymptotic Analysis of the Two-Stage Approach: Towards Data-Driven Parameter Estimation
Braghadeesh Lakshminarayanan, Cristian R. Rojas
https://arxiv.org/abs/2508.18201 https…

On Asymptotic Analysis of the Two-Stage Approach: Towards Data-Driven Parameter Estimation
In this paper, we analyze the asymptotic properties of the Two-Stage (TS) estimator -- a simulation-based parameter estimation method that constructs estimators offline from synthetic data. While TS offers significant computational advantages compared to standard approaches to estimation, its statistical properties have not been previously analyzed in the literature. Under simple assumptions, we establish that the TS estimator is strongly consistent and asymptotically normal, providing the firs…

A Multidimensional Self-Adaptive Numerical Simulation Framework for Semiconductor Boltzmann Transport Equation
Zeyu Zhang, Xiaoyu Zhang, Zhigang Song, Qing Fang
https://arxiv.org/abs/2509.15879

A Multidimensional Self-Adaptive Numerical Simulation Framework for Semiconductor Boltzmann Transport Equation
This research addresses the numerical simulation of the Boltzmann transport equation for semiconductor devices by proposing a multidimensional self-adaptive numerical simulation framework. This framework is applied to two important generalized forms of the equation: a parabolic equation with singular properties on the unit disk and a continuity equation. The study enhances the alignment of numerical simulations with physical characteristics through polar coordinate transformation and variable d…

OpenAIs neue App #Sora2 macht Deepfakes zur Massenware – mit endlosem KI-Videofeed statt Realität. t3n beschreibt treffend, wie Sora zum Symbol einer „AI-Tiktok-Slop-Maschine“ wird. Es geht längst nicht nur um Technik, sondern um die Frage: Wie viel Simulation verträgt unsere Gesellschaft? | #t3n

The simulation is fraying at the edges

Chip design software maker Cadence agrees to acquire Stockholm-based Hexagon's design and simulation software business for ~$3.18B in cash and stock (Nathan Owens/Manufacturing Dive)
https://www.manufacturingdive.com/news/cad

Cadence Design Systems to spend $3.2B on Hexagon’s simulations tech
The acquisition would bolster the industrial software maker’s position in the growing structural analysis market used widely by Boeing, Lockheed Martin, Toyota and other suppliers.

Cowboy Roundup: 3 key matchups vs Commanders, Madden Week 7 simulation https://www.si.com/nfl/cowboys/news/dallas-cowboys-3-key-matchups-commanders-madden-week-7-simulation

Effects of multidimensional treatment of gravity in simulations on supernova gravitational waves
Hajime Sotani, Bernhard M\"uller, Tomoya Takiwaki
https://arxiv.org/abs/2509.19724

Effects of multidimensional treatment of gravity in simulations on supernova gravitational waves
Supernova explosions are expected as one of the promising candidates for gravitational wave sources. In this study, we examine the supernova gravitational waves, focusing on the multidimensional treatment of gravity in the simulation. For this purpose, we newly performed two-dimensional relativistic simulations with a nonmonopole (two-dimensional) potential and compared the resultant gravitational wave signals in the simulations with the frequencies of the proto-neutron stars with and without t…

ZynqParrot: A Scale-Down Approach to Cycle-Accurate, FPGA-Accelerated Co-Emulation
Daniel Ruelas-Petrisko, Farzam Gilani, Anoop Mysore Nataraja, Zoe Taylor, Michael Taylor
https://arxiv.org/abs/2509.20543

ZynqParrot: A Scale-Down Approach to Cycle-Accurate, FPGA-Accelerated Co-Emulation
As processors increase in complexity, costs grow even more rapidly, both for functional verification and performance validation. Most often, silicon characterizations comprise simple performance counters, which are aggregated and separated to tell a story. Based on these inferences, performance engineers employ microarchitectural simulation to inspect deeply into the core. Unfortunately, dramatically longer runtimes make simulation infeasible for long workloads. We propose a Scale-Down approa…

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-…

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…

The Complexity of Deciding Characteristic Formulae Modulo Nested Simulation (extended abstract)
Luca Aceto, Antonis Achilleos, Aggeliki Chalki, Anna Ing\'olfsd\'ottir
https://arxiv.org/abs/2509.14089

The Complexity of Deciding Characteristic Formulae Modulo Nested Simulation (extended abstract)
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. T…

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…

In-Transit Data Transport Strategies for Coupled AI-Simulation Workflow Patterns
Harikrishna Tummalapalli, Riccardo Balin, Christine M. Simpson, Andrew Park, Aymen Alsaadi, Andrew E. Shao, Wesley Brewer, Shantenu Jha
https://arxiv.org/abs/2509.19150

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

An LLM-based Agent Simulation Approach to Study Moral Evolution
Zhou Ziheng, Huacong Tang, Mingjie Bi, Yipeng Kang, Wanying He, Fang Sun, Yizhou Sun, Ying Nian Wu, Demetri Terzopoulos, Fangwei Zhong
https://arxiv.org/abs/2509.17703

An LLM-based Agent Simulation Approach to Study Moral Evolution
The evolution of morality presents a puzzle: natural selection should favor self-interest, yet humans developed moral systems promoting altruism. We address this question by introducing a novel Large Language Model (LLM)-based agent simulation framework modeling prehistoric hunter-gatherer societies. This platform is designed to probe diverse questions in social evolution, from survival advantages to inter-group dynamics. To investigate moral evolution, we designed agents with varying moral dis…

Breaking the Discretization Barrier of Continuous Physics Simulation Learning
Fan Xu, Hao Wu, Nan Wang, Lilan Peng, Kun Wang, Wei Gong, Xibin Zhao
https://arxiv.org/abs/2509.17955

Breaking the Discretization Barrier of Continuous Physics Simulation Learning
The modeling of complicated time-evolving physical dynamics from partial observations is a long-standing challenge. Particularly, observations can be sparsely distributed in a seemingly random or unstructured manner, making it difficult to capture highly nonlinear features in a variety of scientific and engineering problems. However, existing data-driven approaches are often constrained by fixed spatial and temporal discretization. While some researchers attempt to achieve spatio-temporal conti…

Wave Tracing: Generalizing The Path Integral To Wave Optics
Shlomi Steinberg, Matt Pharr
https://arxiv.org/abs/2508.17386 https://arxiv.org/pdf/2508.17386

Wave Tracing: Generalizing The Path Integral To Wave Optics
Modeling the wave nature of light and the propagation and diffraction of electromagnetic fields is crucial for the accurate simulation of many phenomena, yet wave simulations are significantly more computationally complex than classical ray-based models. In this work, we start by analyzing the classical path integral formulation of light transport and rigorously study which wave-optical phenomena can be reproduced by it. We then introduce a bilinear path integral generalization for wave-optical…

The Flagship galaxy mock catalogue - a comprehensive simulation for the #Euclid mission: https://www.aanda.org/articles/aa/full_html/2025/05/aa50853-24/aa50853-24.html -> A virtual Universe for Euclid: https://www.euclid-ec.org/public/press-releases/euclid-flagship-simulations/ - the largest cosmological simulations catalogue is now public.

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Excited-CAFQA: A classical simulation bootstrap for the variational estimation of molecular excited states
Bikrant Bhattacharyya, Gokul Ravi
https://arxiv.org/abs/2509.20588 htt…

Excited-CAFQA: A classical simulation bootstrap for the variational estimation of molecular excited states
Variational Quantum Algorithms (VQAs) are iterative algorithms suited to implementation on current-era quantum devices. VQAs employ classical optimization to minimize cost functions evaluated on quantum circuits. However, the extent to which VQAs manage noise is often insufficient for quantum chemistry applications. One method of improving VQAs is through accurate ansatz initialization. The CAFQA (Clifford Ansatz For Quantum Accuracy) protocol runs a discrete search through a classically simula…

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…

The Syntax and Semantics of einsum
Maurice Wenig, Paul G. Rump, Mark Blacher, Joachim Giesen
https://arxiv.org/abs/2509.20020 https://arxiv.org/pdf/2509.20…

The Syntax and Semantics of einsum
In 2011, einsum was introduced to NumPy as a practical and convenient notation for tensor expressions in machine learning, quantum circuit simulation, and other fields. It has since been implemented in additional Python frameworks such as PyTorch and TensorFlow, as well as in other programming languages such as Julia. Despite its practical success, the einsum notation still lacks a solid theoretical basis, and is not unified across the different frameworks, limiting opportunities for formal rea…

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…

Probing the Parameter Space of Axion-Like Particles Using Simulation-Based Inference
Pooja Bhattacharjee, Christopher Eckner, Gabrijela Zaharijas, Gert Kluge, Giacomo D'Amico
https://arxiv.org/abs/2509.20578

Probing the Parameter Space of Axion-Like Particles Using Simulation-Based Inference
Axion-like particles (ALPs), hypothetical pseudoscalar particles that couple to photons, are among the most actively investigated candidates for new physics beyond the Standard Model. Their interaction with gamma rays in the presence of astrophysical magnetic fields can leave characteristic, energy-dependent modulations in observed spectra. Capturing such subtle features requires precise statistical inference, but standard likelihood-based methods often fall short when faced with complex models…

A Fourier/Modal-Spectral-Element Method for the Simulation of High-Reynolds Number Incompressible Stratified Flows in Domains with a Single Non-Periodic Direction
Nidia Reyes-Gil, Greg Thomsen, Kristopher Rowe, Peter Diamessis
https://arxiv.org/abs/2509.20833

A Fourier/Modal-Spectral-Element Method for the Simulation of High-Reynolds Number Incompressible Stratified Flows in Domains with a Single Non-Periodic Direction
We present the components of a high-order accurate Navier-Stokes solver designed to simulate high-Reynolds-number stratified flows. The proposed numerical model addresses some of the numerical and computational challenges that high-Reynolds-number simulations pose, facilitating the reproduction of stratified turbulent fluid dynamics typically observed in oceanic and atmospheric flows, namely the development of thin regions of high vertical shear, strongly layered turbulence at high Reynolds num…

Cu2XSiS4 (X = Ge, Sn, and Pb) materials for solar-cell applications: A DFT SCAPS-1D simulation
H. Laltlanmawii, L. Celestine, R. Zosiamliana, B. Chettri, S. Bhattarai, K. C. Bhamu, D. P. Rai
https://arxiv.org/abs/2509.20845

Cu2XSiS4 (X = Ge, Sn, and Pb) materials for solar-cell applications: A DFT+SCAPS-1D simulation
By means of the first-principles density functional theory (DFT), I2-II-IV-VI4 type Cu-based quaternary chalcogenides Cu 2 XSiS 4 (X = Ge, Sn, and Pb) have been thoroughly investigated. We report the study of Ge and Sn substitution in the divalent cation site for their potential applications in photovoltaics for the first time. The structural, electronic, optical, and mechanical properties have been calculated. The structural and thermal stability is verified by calculating the elastic constant…

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…

Rich State Observations Empower Reinforcement Learning to Surpass PID: A Drone Ball Balancing Study
Mingjiang Liu, Hailong Huang
https://arxiv.org/abs/2509.21122 https://…

Rich State Observations Empower Reinforcement Learning to Surpass PID: A Drone Ball Balancing Study
This paper addresses a drone ball-balancing task, in which a drone stabilizes a ball atop a movable beam through cable-based interaction. We propose a hierarchical control framework that decouples high-level balancing policy from low-level drone control, and train a reinforcement learning (RL) policy to handle the high-level decision-making. Simulation results show that the RL policy achieves superior performance compared to carefully tuned PID controllers within the same hierarchical structure…

Improving time dynamics simulation by sampling the error unitary
Lana Mineh, Adrian Chapman, Raul A. Santos
https://arxiv.org/abs/2508.17542 https://arxiv.…

Improving time dynamics simulation by sampling the error unitary
We introduce an algorithm to improve the error scaling of product formulas by randomly sampling the generator of their exact error unitary. Our approach takes an arbitrary product formula of time $t$, $S_k(t)$ with error $O(t^{k+1})$ and produces a stochastic formula with expected error scaling as $O(t^{2k+2})$ with respect to the exact dynamics. For a given fixed error $ε$ and total evolution time $T$ this leads to an improved gate complexity of $N=O(T(T/ε)^{\frac{1}{2k+1}})$ compared to the…

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…

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 …

Assessing the Power-Law Emissivity Assumption in X-ray Reflection Spectroscopy: A Simulation-Based Evaluation of Different Coronal Geometries
Songcheng Li, Abdurakhmon Nosirov, Cosimo Bambi, Honghui Liu, Zuobin Zhang, Shafqat Riaz
https://arxiv.org/abs/2509.20752

Assessing the Power-Law Emissivity Assumption in X-ray Reflection Spectroscopy: A Simulation-Based Evaluation of Different Coronal Geometries
The emissivity profile assumed in X-ray reflection spectroscopy significantly impacts black hole spin measurements. Using simulated NuSTAR spectra generated for lamppost and disk-like coronae with the relxill model suite, we evaluate systematic biases introduced when fitting with power-law or broken power-law emissivity profiles. We find that a simple power-law can accurately recover spins for low-height lamppost coronae with long exposures and low inclination angle, while broken power-laws int…

Design Insights and Comparative Evaluation of a Hardware-Based Cooperative Perception Architecture for Lane Change Prediction
Mohamed Manzour, Catherine M. Elias, Omar M. Shehata, Rub\'en Izquierdo, Miguel \'Angel Sotelo
https://arxiv.org/abs/2509.20218

Design Insights and Comparative Evaluation of a Hardware-Based Cooperative Perception Architecture for Lane Change Prediction
Research on lane change prediction has gained attention in the last few years. Most existing works in this area have been conducted in simulation environments or with pre-recorded datasets, these works often rely on simplified assumptions about sensing, communication, and traffic behavior that do not always hold in practice. Real-world deployments of lane-change prediction systems are relatively rare, and when they are reported, the practical challenges, limitations, and lessons learned are oft…

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…

Muonium Spectroscopy as a Quantum Sensor for Ultralight Axion Dark Matter
Feng Fang, Kim Siang Khaw, Ce Zhang, Qiaoli Yang, Liangwen Chen, Jie Yang, Lei Yang, Zhiyu Sun
https://arxiv.org/abs/2509.20862

Muonium Spectroscopy as a Quantum Sensor for Ultralight Axion Dark Matter
High intensity muon beams open new avenues for exploring physics beyond the Standard Model. In this paper, we propose a muonium-based experiment to search for ultralight axions dark matter through resonant quantum transitions between hyperfine states. Combining theoretical calculation with simulation results, we show that a muon beam with a realistic intensity of $10^5 \rm /s$ in the $\mathcal{O}(10~\text{keV})$ energy range could improve constraints on the axion-muon coupling by {up to seven o…

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…

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…

Hybrid Baseband Simulation for Single-Channel Radar-Based Indoor Localization System
Sven Hinderer, Zheming Yin, Athanasios Papanikolaou, Jan Hesselbarth, Bin Yang
https://arxiv.org/abs/2509.15650

Hybrid Baseband Simulation for Single-Channel Radar-Based Indoor Localization System
Indoor localization with chirp sequence radar at millimeter wavelength offers high localization accuracy at low system cost. We propose a hybrid radar baseband signal simulator for our novel single-channel radar-based indoor localization system consisting of an active radar and passive reflectors as references. By combining ray tracing channel simulations with real measurements of the two-way antenna gain of the radar and accurate simulation of the radar cross section of chosen reflectors, real…

EEG-Driven AR-Robot System for Zero-Touch Grasping Manipulation
Junzhe Wang, Jiarui Xie, Pengfei Hao, Zheng Li, Yi Cai
https://arxiv.org/abs/2509.20656 https://

EEG-Driven AR-Robot System for Zero-Touch Grasping Manipulation
Reliable brain-computer interface (BCI) control of robots provides an intuitive and accessible means of human-robot interaction, particularly valuable for individuals with motor impairments. However, existing BCI-Robot systems face major limitations: electroencephalography (EEG) signals are noisy and unstable, target selection is often predefined and inflexible, and most studies remain restricted to simulation without closed-loop validation. These issues hinder real-world deployment in assistiv…

Design Insights and Comparative Evaluation of a Hardware-Based Cooperative Perception Architecture for Lane Change Prediction
Mohamed Manzour, Catherine M. Elias, Omar M. Shehata, Rub\'en Izquierdo, Miguel \'Angel Sotelo
https://arxiv.org/abs/2509.20218

Design Insights and Comparative Evaluation of a Hardware-Based Cooperative Perception Architecture for Lane Change Prediction
Research on lane change prediction has gained attention in the last few years. Most existing works in this area have been conducted in simulation environments or with pre-recorded datasets, these works often rely on simplified assumptions about sensing, communication, and traffic behavior that do not always hold in practice. Real-world deployments of lane-change prediction systems are relatively rare, and when they are reported, the practical challenges, limitations, and lessons learned are oft…

Dynamical properties of oscillating, viscous, transonic accretion disks around black holes
Sanjit Debnath, Indranil Chattopadhyay, Raj Kishor Joshi, Philippe Laurent, Priyesh Kumar Tripathi, M. Saleem Khan
https://arxiv.org/abs/2509.19934

Dynamical properties of oscillating, viscous, transonic accretion disks around black holes
We investigate the time evolution of sub-Keplerian transonic accretion flow onto a non-rotating black hole using axisymmetric viscous hydrodynamic simulations. We simulate the accretion flow using boundary values from semi-analytical analysis and set up three different models. Two of the models do not predict accretion shocks from the semi-analytic analysis, while one of them does. We also consider radiative cooling along with viscosity in the simulation. Our two-dimensional simulation deviated…

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…

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…

Parse-Augment-Distill: Learning Generalizable Bimanual Visuomotor Policies from Single Human Video
Georgios Tziafas, Jiayun Zhang, Hamidreza Kasaei
https://arxiv.org/abs/2509.20286

Parse-Augment-Distill: Learning Generalizable Bimanual Visuomotor Policies from Single Human Video
Learning visuomotor policies from expert demonstrations is an important frontier in modern robotics research, however, most popular methods require copious efforts for collecting teleoperation data and struggle to generalize out-ofdistribution. Scaling data collection has been explored through leveraging human videos, as well as demonstration augmentation techniques. The latter approach typically requires expensive simulation rollouts and trains policies with synthetic image data, therefore int…

GPU Acceleration for Faster Evolutionary Spatial Cyclic Game Systems
Louie Sinadjan
https://arxiv.org/abs/2508.16639 https://arxiv.org/pdf/2508.16639

GPU Acceleration for Faster Evolutionary Spatial Cyclic Game Systems
This dissertation presents the design, implementation and evaluation of GPU-accelerated simulation frameworks for Evolutionary Spatial Cyclic Games (ESCGs), a class of agent-based models used to study ecological and evolutionary dynamics. Traditional single-threaded ESCG simulations are computationally expensive and scale poorly. To address this, high-performance implementations were developed using Apple's Metal and Nvidia's CUDA, with a validated single-threaded C++ version serving as a basel…

Validation of a Reynolds-averaged numerical simulation environment to simulate high-pressure, auto-igniting hydrogen diffusion flames
N. Diepstraten, L. M. T. Somers, J. A. van Oijen
https://arxiv.org/abs/2509.18841

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…

Connecting Quantum Computing with Classical Stochastic Simulation
Jose Blanchet, Mark S. Squillante, Mario Szegedy, Guanyang Wang
https://arxiv.org/abs/2509.18614 https://

TopoCut: Learning Multi-Step Cutting with Spectral Rewards and Discrete Diffusion Policies
Liquan Wang, Jiangjie Bian, Eric Heiden, Animesh Garg
https://arxiv.org/abs/2509.19712

TopoCut: Learning Multi-Step Cutting with Spectral Rewards and Discrete Diffusion Policies
Robotic manipulation tasks involving cutting deformable objects remain challenging due to complex topological behaviors, difficulties in perceiving dense object states, and the lack of efficient evaluation methods for cutting outcomes. In this paper, we introduce TopoCut, a comprehensive benchmark for multi-step robotic cutting tasks that integrates a cutting environment and generalized policy learning. TopoCut is built upon three core components: (1) We introduce a high-fidelity simulation env…

Merger-Induced Disturbance and Temporal Signatures in Galaxy Clusters: A Combined Phase Space and Photometric Analysis
Chuiyang Kong, Ian Dell'Antonio
https://arxiv.org/abs/2509.20637

Merger-Induced Disturbance and Temporal Signatures in Galaxy Clusters: A Combined Phase Space and Photometric Analysis
We present a physically interpretable framework to quantify dynamical disturbances in galaxy clusters using projected two-dimensional phase-space information. Based on the TNG-Cluster simulation, we construct a disturbance score that captures merger-driven asymmetries through features such as velocity dispersion and Gaussian Mixture Model (GMM) peak fitting, which captures asymmetries indicative of dynamical disturbance. All features are derived from observable quantities and are intended to be…

Transversal STAR architecture for megaquop-scale quantum simulation with neutral atoms
Refaat Ismail, I-Chi Chen, Chen Zhao, Ronen Weiss, Fangli Liu, Hengyun Zhou, Sheng-Tao Wang, Andrew Sornborger, Milan Kornja\v{c}a
https://arxiv.org/abs/2509.18294

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…

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…

Quantum Random Synthetic Skyrmion Texture Generation, a Qiskit Simulation
Hillol Biswas
https://arxiv.org/abs/2509.18947 https://arxiv.org/pdf/2509.18947…

Quantum Random Synthetic Skyrmion Texture Generation, a Qiskit Simulation
An integer winding, i.e., topological charge, is a characteristic of skyrmions, which are topologically nontrivial spin patterns in magnets. They emerge when smooth two-dimensional spin configurations are stabilized by conflicting interactions such as exchange, anisotropy, the Dzyaloshinskii-Moriya interaction, or geometric frustration. These nanoscale textures, which are typically a few to tens of nanometers in size, are strong 'particle-like' excitations because they are shielded by energy ba…

Trace Replay Simulation of MIT SuperCloud for Studying Optimal Sustainability Policies
Wesley Brewer, Matthias Maiterth, Damien Fay
https://arxiv.org/abs/2509.16513 https://

Trace Replay Simulation of MIT SuperCloud for Studying Optimal Sustainability Policies
The rapid growth of AI supercomputing is creating unprecedented power demands, with next-generation GPU datacenters requiring hundreds of megawatts and producing fast, large swings in consumption. To address the resulting challenges for utilities and system operators, we extend ExaDigiT, an open-source digital twin framework for modeling power, cooling, and scheduling of supercomputers. Originally developed for replaying traces from leadership-class HPC systems, ExaDigiT now incorporates hetero…

The influence of boundary conditions and interfacial slip on the time taken to achieve a nonequilibrium steady-state for highly confined flows
Carmelo Riccardo Civello, Luca Maffioli, Joseph Johnson, Edward R. Smith, James P. Ewen, Peter J. Daivis, Daniele Dini, B. D. Todd
https://arxiv.org/abs/2509.20944

The influence of boundary conditions and interfacial slip on the time taken to achieve a nonequilibrium steady-state for highly confined flows
We investigate the equilibration time to attain steady-state for a system of liquid molecules under boundary-driven planar Couette flow via nonequilibrium molecular dynamics (NEMD) simulation. In particular, we examine the equilibration time for the two common types of boundary driven flow: one in which both walls slide with equal and opposite velocity, and the other in which one wall is fixed and the other moves with twice the velocity. Both flows give identical steady-state strain rates, and …

UniTac2Pose: A Unified Approach Learned in Simulation for Category-level Visuotactile In-hand Pose Estimation
Mingdong Wu, Long Yang, Jin Liu, Weiyao Huang, Lehong Wu, Zelin Chen, Daolin Ma, Hao Dong
https://arxiv.org/abs/2509.15934

UniTac2Pose: A Unified Approach Learned in Simulation for Category-level Visuotactile In-hand Pose Estimation
Accurate estimation of the in-hand pose of an object based on its CAD model is crucial in both industrial applications and everyday tasks, ranging from positioning workpieces and assembling components to seamlessly inserting devices like USB connectors. While existing methods often rely on regression, feature matching, or registration techniques, achieving high precision and generalizability to unseen CAD models remains a significant challenge. In this paper, we propose a novel three-stage fram…

Structured Quantum Baths with Memory: A QuTiP Framework for Spectral Diagnostics and Machine Learning Inference
Ridwan Sakidja
https://arxiv.org/abs/2508.17514 https://

Structured Quantum Baths with Memory: A QuTiP Framework for Spectral Diagnostics and Machine Learning Inference
We introduce a compact simulation framework for modeling open quantum systems coupled to structured, memory-retaining baths using QuTiP. Our method models the bath as a finite set of layered qubits with adjustable connections, interpreted either as a physical realization or as a conceptual representation, rather than as a continuum. This explicit modeling enables direct control over non-Markovian dynamics and allows spectral diagnostics via Fast Fourier Transform (FFT) of system observables. Us…

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…

HARLI CQUINN: Higher Adjusted Randomness with Linear In Complexity QUantum INspired Networks for K-Means
Jiten Oswal, Saumya Biswas
https://arxiv.org/abs/2509.19395 https://

HARLI CQUINN: Higher Adjusted Randomness with Linear In Complexity QUantum INspired Networks for K-Means
We contrast a minimalistic implementation of quantum k-means algorithm to classical k-means algorithm. With classical simulation results, we demonstrate a quantum performance, on and above par, with the classical k-means algorithm. We present benchmarks of its accuracy for test cases of both well-known and experimental datasets. Despite extensive research into quantum k-means algorithms, our approach reveals previously unexplored methodological improvements. The encoding step can be minimalisti…

Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[2/2]:
- Efficient Preparation of Resource States for Hamiltonian Simulation and Universal Quantum Computa...
Thierry N. Kaldenbach, Isaac D. Smith, Hendrik Poulsen Nautrup, Matthias Heller, Hans J. Briegel
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