Tootfinder

Madden NFL 26 season simulation: Hail Mary wins Super Bowl LX; Eagles implode and Jaxson Dart breaks out

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

Bridging Research and Practice in Simulation-based Testing of Industrial Robot Navigation Systems
Sajad Khatiri, Francisco Eli Vina Barrientos, Maximilian Wulf, Paolo Tonella, Sebastiano Panichella
https://arxiv.org/abs/2510.09396

Bridging Research and Practice in Simulation-based Testing of Industrial Robot Navigation Systems
Ensuring robust robotic navigation in dynamic environments is a key challenge, as traditional testing methods often struggle to cover the full spectrum of operational requirements. This paper presents the industrial adoption of Surrealist, a simulation-based test generation framework originally for UAVs, now applied to the ANYmal quadrupedal robot for industrial inspection. Our method uses a search-based algorithm to automatically generate challenging obstacle avoidance scenarios, uncovering fa…

Mapping the Urban Mobility Intelligence Frontier: A Scientometric Analysis of Data-Driven Pedestrian Trajectory Prediction and Simulation
Junhao Xu, Hui Zeng
https://arxiv.org/abs/2510.10327

Mapping the Urban Mobility Intelligence Frontier: A Scientometric Analysis of Data-Driven Pedestrian Trajectory Prediction and Simulation
Understanding and predicting pedestrian dynamics has become essential for shaping safer, more responsive, and human-centered urban environments. This study conducts a comprehensive scientometric analysis of research on data-driven pedestrian trajectory prediction and crowd simulation, mapping its intellectual evolution and interdisciplinary structure. Using bibliometric data from the Web of Science Core Collection, we employ SciExplorer and Bibliometrix to identify major trends, influential con…

StoryBox: Collaborative Multi-Agent Simulation for Hybrid Bottom-Up Long-Form Story Generation Using Large Language Models
Zehao Chen, Rong Pan, Haoran Li
https://arxiv.org/abs/2510.11618

StoryBox: Collaborative Multi-Agent Simulation for Hybrid Bottom-Up Long-Form Story Generation Using Large Language Models
Human writers often begin their stories with an overarching mental scene, where they envision the interactions between characters and their environment. Inspired by this creative process, we propose a novel approach to long-form story generation, termed hybrid bottom-up long-form story generation, using multi-agent simulations. In our method, agents interact within a dynamic sandbox environment, where their behaviors and interactions with one another and the environment generate emergent events…

Taylor Swift shares some NFL knowledge, plus 2025 season goes off the rails in our Madden simulation

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

Efficient predecision scheme for Metropolis Monte Carlo simulation of long-range interacting lattice systems
Fabio M\"uller, Wolfhard Janke
https://arxiv.org/abs/2508.09775

Efficient predecision scheme for Metropolis Monte Carlo simulation of long-range interacting lattice systems
We propose a fast and general predecision scheme for Metropolis Monte Carlo simulation of $d$-dimensional long-range interacting lattice models. For potentials of the form $V(r)=r^{-d-σ}$, this reduces the computational complexity from $O\left(N^2\right)$ to $O\left(N^{2-σ/d}\right)$ for $σ< d$ and to $O\left(N \right)$ for $σ> d$, respectively. The algorithm is implemented and tested for several $\mathrm{O}(n)$ spin models ranging from the Ising over the XY to the Edwards-Anderson spin-gla…

Entangled Photon Pair Generator via Biexciton-Exciton Cascade in Semiconductor Quantum Dots and its Simulation
Simon Sekav\v{c}nik, Paul Kohl, Janis N\"otzel
https://arxiv.org/abs/2510.09262

Entangled Photon Pair Generator via Biexciton-Exciton Cascade in Semiconductor Quantum Dots and its Simulation
The generation of entangled photon pairs is highly useful for many types of quantum technologies. In this work an entangled photon pair generator that utilises the biexciton-exciton cascade in semiconductor quantum dots is described on a physical, mathematical, and software level. The system is implemented and simulated as a self-contained component in a framework for bigger quantum optical experiments. Thus, it is a description to further the holistic understanding of the system for interdisci…

Frenkel anomaly on co-ordination numbers in liquid CO2 at 100 and 1000 bar studied by Monte Carlo simulation using Kihara potential model
Koji Kobashi
https://arxiv.org/abs/2510.11029

Frenkel anomaly on co-ordination numbers in liquid CO2 at 100 and 1000 bar studied by Monte Carlo simulation using Kihara potential model
An issue concerning the Frenkel line of liquid CO2 is that its location has not been unequivocally determined. So far, reliable Frenkel lines were identified from velocity autocorrelation functions (VAFs) computed by Molecular Dynamic simulations; however VAFs cannot be directly verified experimentally. By contrast, the co-ordination numbers (CNs) can be measured experimentally by X-ray and neutron scattering or computed by Monte Carlo (MC) simulation, and thus provide an alternative means of d…

The Importance of Being Adaptable: An Exploration of the Power and Limitations of Domain Adaptation for Simulation-Based Inference with Galaxy Clusters
Michelle Ntampaka, A. Ciprijanovic, Ana Maria Delgado, John Soltis, John F. Wu, Mikaeel Yunus, John ZuHone
https://arxiv.org/abs/2510.09748

The Importance of Being Adaptable: An Exploration of the Power and Limitations of Domain Adaptation for Simulation-Based Inference with Galaxy Clusters
The application of deep machine learning methods in astronomy has exploded in the last decade, with new models showing remarkably improved performance on benchmark tasks. Not nearly enough attention is given to understanding the models' robustness, especially when the test data are systematically different from the training data, or "out of domain." Domain shift poses a significant challenge for simulation-based inference, where models are trained on simulated data but applied to real observati…

Lifted Heston Model: Efficient Monte Carlo Simulation with Large Time Steps
Nicola F. Zaugg, Lech A. Grzelak
https://arxiv.org/abs/2510.08805 https://arxiv…

Lifted Heston Model: Efficient Monte Carlo Simulation with Large Time Steps
The lifted Heston model is a stochastic volatility model emerging as a Markovian lift of the rough Heston model and the class of rough volatility processes. The model encodes the path dependency of volatility on a set of N square-root state processes driven by a common stochastic factor. While the system is Markovian, simulation schemes such as the Euler scheme exist, but require a small-step, multidimensional simulation of the state processes and are therefore numerically challenging. We propo…

Unlocking Thickness Modeling for Codimensional Contact Simulation
Gonzalo Gomez-Nogales, Zhen Chen, Rosalie Martin, Elena Garces, Danny M. Kaufman
https://arxiv.org/abs/2510.10256

Unlocking Thickness Modeling for Codimensional Contact Simulation
In this work we analyze and address a fundamental restriction that blocks the reliable application of codimensional yarn-level and shell models with thickness, to simulate real-world woven and knit fabrics. As discretizations refine toward practical and accurate physical modeling, such models can generate non-physical contact forces with stencil-neighboring elements in the simulation mesh, leading to severe locking artifacts. While not well-documented in the literature, this restriction has so …

Designing and Evaluating an AI-driven Immersive Multidisciplinary Simulation (AIMS) for Interprofessional Education
Ruijie Wang, Jie Lu, Bo Pei, Evonne Jones, Jamey Brinson, Timothy Brown
https://arxiv.org/abs/2510.08891

Designing and Evaluating an AI-driven Immersive Multidisciplinary Simulation (AIMS) for Interprofessional Education
Interprofessional education has long relied on case studies and the use of standardized patients to support teamwork, communication, and related collaborative competencies among healthcare professionals. However, traditional approaches are often limited by cost, scalability, and inability to mimic the dynamic complexity of real-world clinical scenarios. To address these challenges, we designed and developed AIMS (AI-Enhanced Immersive Multidisciplinary Simulations), a virtual simulation that in…

Only 60GB of RAM free but I was going to do some more delayering and chip imaging. Might have to run the stitch on my other box...
hashtag bigdataproblems or something?
To be fair I did *somewhat* bring this on myself running an EM solver simulation while editing a video and doing a board design and viewing a bunch of other big images and such. But still, annoying :p

Hybrid-Precision Block-Jacobi Preconditioned GMRES Solver for Linear System in Circuit Simulation
Zijian Zhang, Rui Hong, Xuesong Chen, Shuting Cai
https://arxiv.org/abs/2509.09139

Hybrid-Precision Block-Jacobi Preconditioned GMRES Solver for Linear System in Circuit Simulation
As integrated circuits become increasingly complex, the demand for efficient and accurate simulation solvers continues to rise. Traditional solvers often struggle with large-scale sparse systems, leading to prolonged simulation times and reduced accuracy. In this paper, a hybrid-precision block-Jacobi preconditioned GMRES solver is proposed to solve the large sparse system in circuit simulation. The proposed method capitalizes on the structural sparsity and block properties of circuit matrices,…

A geometric obstruction to self-simulation for groups
Sebasti\'an Barbieri, Kan\'eda Blot, Mathieu Sablik, Ville Salo
https://arxiv.org/abs/2510.10291 https://

A geometric obstruction to self-simulation for groups
We introduce a new quasi-isometry invariant for finitely generated groups and show that every group with this property admits a subshift which is effectively closed by patterns and that cannot be realized as the topological factor of any subshift of finite type. We provide several examples of groups with the property, such as amenable groups, multi-ended groups, generalized Baumslag-Solitar groups, fundamental groups of surfaces, and cocompact Fuchsian groups.

Design and Simulation of 6T SRAM Array
Justin London
https://arxiv.org/abs/2508.09419 https://arxiv.org/pdf/2508.09419

Design and Simulation of 6T SRAM Array
Conventional 6T SRAM is used in microprocessors in the cache memory design. The basic 6T SRAM cell and a 6 bit memory array layout are designed in LEdit. The design and analysis of key SRAM components, sense amplifiers, decoders, write drivers and precharge circuits are also provided. The pulse voltage waveforms generated for read and write operations as well as Q and Qbar nodes are simulated in LTSpice. Parasitic capacitances are extracted and their impact on the waveforms analyzed. Static noi…

People use fast, flat goal-directed simulation to reason about novel problems
Katherine M. Collins, Cedegao E. Zhang, Lionel Wong, Mauricio Barba da Costa, Graham Todd, Adrian Weller, Samuel J. Cheyette, Thomas L. Griffiths, Joshua B. Tenenbaum
https://arxiv.org/abs/2510.11503

People use fast, flat goal-directed simulation to reason about novel problems
Games have long been a microcosm for studying planning and reasoning in both natural and artificial intelligence, especially with a focus on expert-level or even super-human play. But real life also pushes human intelligence along a different frontier, requiring people to flexibly navigate decision-making problems that they have never thought about before. Here, we use novice gameplay to study how people make decisions and form judgments in new problem settings. We show that people are systemat…

FOGMACHINE -- Leveraging Discrete-Event Simulation and Scene Graphs for Modeling Hierarchical, Interconnected Environments under Partial Observations from Mobile Agents
Lars Ohnemus, Nils Hantke, Max Wei{\ss}er, Kai Furmans
https://arxiv.org/abs/2510.09483

FOGMACHINE -- Leveraging Discrete-Event Simulation and Scene Graphs for Modeling Hierarchical, Interconnected Environments under Partial Observations from Mobile Agents
Dynamic Scene Graphs (DSGs) provide a structured representation of hierarchical, interconnected environments, but current approaches struggle to capture stochastic dynamics, partial observability, and multi-agent activity. These aspects are critical for embodied AI, where agents must act under uncertainty and delayed perception. We introduce FOGMACHINE , an open-source framework that fuses DSGs with discrete-event simulation to model object dynamics, agent observations, and interactions at scal…

Impact of Wind Direction on Flow Over a Realistic Urban Area: A Large-Eddy Simulation Study
Ivette Rodr\'iguez, Josep Maria Dur\'o, Ernest Mestres, Ming Teng, Oriol Lehmkuhl
https://arxiv.org/abs/2510.11247

Impact of Wind Direction on Flow Over a Realistic Urban Area: A Large-Eddy Simulation Study
We conducted high-resolution large-eddy simulations over a real urban district in Barcelona to examine the impact of wind direction on near-ground flow. The computational mesh resolves over 500 million degrees of freedom, with a spatial resolution on the order of 1 m at pedestrian level. This allows a detailed analysis of mean velocity and turbulence patterns within the canopy layer. Although instantaneous flow fields differ significantly between cases, double-averaged profiles of velocity and …

What is the contribution of gravitational infall on the mass assembly of star-forming clouds? A case study in a numerical simulation of the interstellar medium
No\'e Brucy, Enrique V\'azquez-Semadeni, Tine Colman, J\'er\'emy Fensch, Ralf S. Klessen
https://arxiv.org/abs/2510.09480

What is the contribution of gravitational infall on the mass assembly of star-forming clouds? A case study in a numerical simulation of the interstellar medium
Star formation in galaxies is a complex phenomenon occurring on a very wide range of scales, and molecular clouds are at the heart of this process. The formation of these structures and the subsequent collapse of the gas within them to form new stars remain unresolved scientific questions. In particular, the role and importance of gravity at between the disk scale height and prestellar cores (100 to 0.01 pc) are still topics of debate. In this work, we conduct a case study examining the mass as…

Simulation of Repair on Dynamic Patient-Specific Left Atrioventricular Valve Models
Stephen Ching, Christopher Zelonis, Christian Herz, Patricia Sabin an Matthew Daemer, Muhammad Nuri, Yan Wang, Devin W. Laurence, Jonathan Chen, Lindsay Rogers, Michael Quartermain, John Moore, Terry Peters, Elvis Chen, Matthew Jolley
https://arxiv.org/abs/…

Simulation of Repair on Dynamic Patient-Specific Left Atrioventricular Valve Models
Purpose: To develop and evaluate a dynamic, image-derived patient-specific physical simulation platform for the assessment of left atrioventricular valve (LAVV) repair strategies in pediatric patients with repaired atrioventricular canal defects. Methods: 3D transesophageal echocardiographic images of two patients with regurgitant LAVVs were identified from an institutional database. Custom code in SlicerHeart was used to segment leaflets, define the annulus, and generate patient-specific valve…

A Multi-Simulation Bridge for IoT Digital Twins
Marco Picone, Samuele Burattini, Marco Melloni, Prasad Talasila, Davide Ziglioli, Matteo Martinelli, Nicola Bicocchi, Alessandro Ricci, Peter Gorm Larsen
https://arxiv.org/abs/2510.08164

A Multi-Simulation Bridge for IoT Digital Twins
The increasing capabilities of Digital Twins (DTs) in the context of the Internet of Things (IoT) and Industrial IoT (IIoT) call for seamless integration with simulation platforms to support system design, validation, and real-time operation. This paper introduces the concept, design, and experimental evaluation of the DT Simulation Bridge - a software framework that enables diverse interaction patterns between active DTs and simulation environments. The framework supports both the DT developme…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/5]:
- UltraRay: Introducing Full-Path Ray Tracing in Physics-Based Ultrasound Simulation
Felix Duelmer, Mohammad Farid Azampour, Magdalena Wysocki, Nassir Navab

Replaced article(s) found for cs.PL. https://arxiv.org/list/cs.PL/new
[1/1]:
- CRDT Emulation, Simulation, and Representation Independence
Nathan Liittschwager, Jonathan Castello, Stelios Tsampas, Lindsey Kuper

Student Development Agent: Risk-free Simulation for Evaluating AIED Innovations
Jianxiao Jiang, Yu Zhang
https://arxiv.org/abs/2510.09183 https://arxiv.org…

Student Development Agent: Risk-free Simulation for Evaluating AIED Innovations
In the age of AI-powered educational (AIED) innovation, evaluating the developmental consequences of novel designs before they are exposed to students has become both essential and challenging. Since such interventions may carry irreversible effects, it is critical to anticipate not only potential benefits but also possible harms. This study proposes a student development agent framework based on large language models (LLMs), designed to simulate how students with diverse characteristics may ev…

A Hybrid Analytical Framework for Asymmetric Pressure and Boundary Layer Wind Simulation in Nor'easters
Seyedeh Fatemeh Mirfakhar, Reda Snaiki, Frank Lombardo
https://arxiv.org/abs/2509.09556

A Hybrid Analytical Framework for Asymmetric Pressure and Boundary Layer Wind Simulation in Nor'easters
Nor'easters frequently impact the North American East Coast, bringing hazardous precipitation, winds, and coastal flooding. Accurate simulation of their pressure and wind fields is essential for forecasting, risk assessment, and infrastructure planning, yet remains challenging due to their complex, asymmetric structure. This study introduces a novel hybrid analytical-data-driven model designed to efficiently simulate Nor'easter pressure and boundary layer wind fields. The pressure field is mode…

Deep Generative Models for Discrete Genotype Simulation
Sihan Xie (GABI), Thierry Tribout (GABI), Didier Boichard (GABI), Blaise Hanczar (IBISC), Julien Chiquet (MIA Paris-Saclay), Eric Barrey (GABI)
https://arxiv.org/abs/2508.09212

Deep Generative Models for Discrete Genotype Simulation
Deep generative models open new avenues for simulating realistic genomic data while preserving privacy and addressing data accessibility constraints. While previous studies have primarily focused on generating gene expression or haplotype data, this study explores generating genotype data in both unconditioned and phenotype-conditioned settings, which is inherently more challenging due to the discrete nature of genotype data. In this work, we developed and evaluated commonly used generative mod…

Data-driven generative simulation of SDEs using diffusion models
Xuefeng Gao, Jiale Zha, Xun Yu Zhou
https://arxiv.org/abs/2509.08731 https://arxiv.org/pdf…

Data-driven generative simulation of SDEs using diffusion models
This paper introduces a new approach to generating sample paths of unknown stochastic differential equations (SDEs) using diffusion models, a class of generative AI models commonly employed in image and video applications. Unlike the traditional Monte Carlo methods for simulating SDEs, which require explicit specifications of the drift and diffusion coefficients, our method takes a model-free, data-driven approach. Given a finite set of sample paths from an SDE, we utilize conditional diffusion…

Simulation: Radioteleskope auf dem Mond könnten Rätsel der Dunklen Materie lösen
Nach dem Urknall hat sich die Dunkle Materie verklumpt, Spuren davon soll es geben. Weil sie nicht von der Erde aus nachweisbar sind, gibt es einen Vorschlag.

In situ estimation of the acoustic surface impedance using simulation-based inference
Jonas M. Schmid, Johannes D. Schmid, Martin Eser, Steffen Marburg
https://arxiv.org/abs/2509.08873

In situ estimation of the acoustic surface impedance using simulation-based inference
Accurate acoustic simulations of enclosed spaces require precise boundary conditions, typically expressed through surface impedances for wave-based methods. Conventional measurement techniques often rely on simplifying assumptions about the sound field and mounting conditions, limiting their validity for real-world scenarios. To overcome these limitations, this study introduces a Bayesian framework for the in situ estimation of frequency-dependent acoustic surface impedances from sparse interio…

What's Coming Next? Short-Term Simulation of Business Processes from Current State
Maksym Avramenko, David Chapela-Campa, Marlon Dumas, Fredrik Milani
https://arxiv.org/abs/2509.07747

What's Coming Next? Short-Term Simulation of Business Processes from Current State
Business process simulation is an approach to evaluate business process changes prior to implementation. Existing methods in this field primarily support tactical decision-making, where simulations start from an empty state and aim to estimate the long-term effects of process changes. A complementary use-case is operational decision-making, where the goal is to forecast short-term performance based on ongoing cases and to analyze the impact of temporary disruptions, such as demand spikes and sh…

Neutrino signal from the hadron-quark phase transition in the conversion of Neutron Stars into Quark Stars
Yossef Zenati, Conrado Albertus Torres, Joseph Silk, M. \'Angeles P\'erez-Garc\'ia
https://arxiv.org/abs/2510.08707

Neutrino signal from the hadron-quark phase transition in the conversion of Neutron Stars into Quark Stars
We calculate in a 1D General Relativistic (GR) hydrodynamic simulation the neutrino luminosity in an astrophysical scenario where a neutron star (NS) displays a hadron-quark phase transition (HQPT) into a Quark Star (QS). Deconfinement is triggered once the central density exceeds a critical threshold above $\sim 3n_0$ being $n_0$, saturation density. We use descriptions based on DD2 and the MIT Bag model equations of state (EOSs). We account for neutrinos using a microphysics forward emission …

Towards Quantum Simulations of Sphaleron Dynamics at Colliders
Min Huang, Ying-Ying Li, Yandong Liu, Hao Zhang
https://arxiv.org/abs/2510.08910 https://arx…

Towards Quantum Simulations of Sphaleron Dynamics at Colliders
Sphaleron dynamics in the Standard Model at high-energy particle collisions remains experimentally unobserved, with theoretical predictions hindered by its nonperturbative real-time nature. In this work, we investigate a quantum simulation approach to this challenge. Taking the $1+1$D $O(3)$ model as a protocol towards studying dynamics of sphaleron in the electroweak theory, we identify the sphaleron configuration and establish lattice parameters that reproduce continuum sphaleron energies wit…

Parameterized crack modelling based on a localized non-intrusive reduced basis method
Margarita Chasapi
https://arxiv.org/abs/2510.10624 https://arxiv.org/…

Parameterized crack modelling based on a localized non-intrusive reduced basis method
This contribution presents a model order reduction strategy for fast parametric modelling of problems with cracks formulated on spline discretizations. In the context of damage detection, parametric reduced order models (ROMs) are well suited for fast computations by establishing an efficient offline/online split of the simulation process. The problems of interest focus on geometric parameters that describe the crack configuration and may pose challenges to constructing efficient ROMs. This wor…

ISAAC: Intelligent, Scalable, Agile, and Accelerated CPU Verification via LLM-aided FPGA Parallelism
Jialin Sun, Yuchen Hu, Dean You, Yushu Du, Hui Wang, Xinwei Fang, Weiwei Shan, Nan Guan, Zhe Jiang
https://arxiv.org/abs/2510.10225

ISAAC: Intelligent, Scalable, Agile, and Accelerated CPU Verification via LLM-aided FPGA Parallelism
Functional verification is a critical bottleneck in integrated circuit development, with CPU verification being especially time-intensive and labour-consuming. Industrial practice relies on differential testing for CPU verification, yet faces bottlenecks at nearly each stage of the framework pipeline: front-end stimulus generation lacks micro-architectural awareness, yielding low-quality and redundant tests that impede coverage closure and miss corner cases. Meanwhile, back-end simulation infra…

Numerical analysis of the homogeneous Landau equation: approximation, error estimates and simulation
Francis Filbet (IMT), Yanzhi Gui (THU), Ling-Bing He (THU)
https://arxiv.org/abs/2509.09276

Numerical analysis of the homogeneous Landau equation: approximation, error estimates and simulation
We construct a numerical solution to the spatially homogeneous Landau equation with Coulomb potential on a domain $D_L$ with N retained Fourier modes. By deriving an explicit error estimate in terms of $L$ and $N$, we demonstrate that for any prescribed error tolerance and fixed time interval $[0, T ]$, there exist choices of $D_L$ and $N$ satisfying explicit conditions such that the error between the numerical and exact solutions is below the tolerance. Specifically, the estimate shows that su…

Towards Automated and Predictive Network-Level Energy Profiling in Reconfigurable IoT Systems
Mohammud J. Bocus, Senhui Qiu, Robert J. Piechocki, Kerstin Eder
https://arxiv.org/abs/2510.09842

Towards Automated and Predictive Network-Level Energy Profiling in Reconfigurable IoT Systems
Energy efficiency has emerged as a defining constraint in the evolution of sustainable Internet of Things (IoT) networks. This work moves beyond simulation-based or device-centric studies to deliver measurement-driven, network-level smart energy analysis. The proposed system enables end-to-end visibility of energy flows across distributed IoT infrastructures, uniting Bluetooth Low Energy (BLE) and Visible Light Communication (VLC) modes with environmental sensing and E-ink display subsystems un…

Multispectral CT Denoising via Simulation-Trained Deep Learning: Experimental Results at the ESRF BM18
Peter G\"anz, Steffen Kie{\ss}, Guangpu Yang, Jajnabalkya Guhathakurta, Tanja Pienkny, Charls Clark, Paul Tafforeau, Andreas Balles, Astrid H\"olzing, Simon Zabler, Sven Simon
https://arxiv.org/abs/2509.08528

Multispectral CT Denoising via Simulation-Trained Deep Learning: Experimental Results at the ESRF BM18
Multispectral computed tomography (CT) enables advanced material characterization by acquiring energy-resolved projection data. However, since the incoming X-ray flux is be distributed across multiple narrow energy bins, the photon count per bin is greatly reduced compared to standard energy-integrated imaging. This inevitably introduces substantial noise, which can either prolong acquisition times and make scan durations infeasible or degrade image quality with strong noise artifacts. To addre…

Astigmatism-free 3D Optical Tweezer Control for Rapid Atom Rearrangement
Yue-Hui Lu, Nathan Song, Tai Xiang, Jacquelyn Ho, Tsai-Chen Lee, Zhenjie Yan, Dan M. Stamper-Kurn
https://arxiv.org/abs/2510.11451

Astigmatism-free 3D Optical Tweezer Control for Rapid Atom Rearrangement
Reconfigurable arrays of neutral atoms are a leading platform for quantum computing, quantum simulation, and quantum metrology. The most common method for atom reconfiguration using optical tweezers relies on frequency chirping of acousto-optic deflectors (AODs). However, chirp-induced acoustic lensing limits the speed of atom transport by deformation of the tweezer profile and warping of the tweezer trajectory. We use a three-dimensional acousto-optic deflector lens (3D-AODL) to mitigate both …

A new simulation from the European Central Bank shows that the digital euro could trigger a deposit outflow of up to €700B during a run on commercial banks (Reuters)
https://www.reuters.com/business/finance/digital…

Toward Human-Robot Teaming: Learning Handover Behaviors from 3D Scenes
Yuekun Wu, Yik Lung Pang, Andrea Cavallaro, Changjae Oh
https://arxiv.org/abs/2508.09855 https://

Toward Human-Robot Teaming: Learning Handover Behaviors from 3D Scenes
Human-robot teaming (HRT) systems often rely on large-scale datasets of human and robot interactions, especially for close-proximity collaboration tasks such as human-robot handovers. Learning robot manipulation policies from raw, real-world image data requires a large number of robot-action trials in the physical environment. Although simulation training offers a cost-effective alternative, the visual domain gap between simulation and robot workspace remains a major limitation. We introduce a …

UltraScatter: Ray-Based Simulation of Ultrasound Scattering
Felix Duelmer, Mohammad Farid Azampour, Nassir Navab
https://arxiv.org/abs/2510.10612 https://a…

UltraScatter: Ray-Based Simulation of Ultrasound Scattering
Traditional ultrasound simulation methods solve wave equations numerically, achieving high accuracy but at substantial computational cost. Faster alternatives based on convolution with precomputed impulse responses remain relatively slow, often requiring several minutes to generate a full B-mode image. We introduce UltraScatter, a probabilistic ray tracing framework that models ultrasound scattering efficiently and realistically. Tissue is represented as a volumetric field of scattering probabi…

Simulating dynamic bonding in soft materials
Tyla R. Holoman, B. P. Prajwal, Glen M. Hocky, Thomas M. Truskett
https://arxiv.org/abs/2510.08879 https://arx…

Simulating dynamic bonding in soft materials
Dynamic bonding is an essential feature of many soft materials. Molecular simulations have proven to be a powerful tool for modeling bonding kinetics and thermodynamics in these materials, providing insights into their properties that cannot be obtained by experiments alone. Here, we review recent advances in modeling dynamic bonding in soft matter via molecular dynamics, Monte Carlo, and hybrid simulation methods, highlighting outstanding challenges and future directions.

Sensitivity-Driven Adaptive Surrogate Modeling for Simulation and Optimization of Dynamical Systems
Jonathan R. Cangelosi, Matthias Heinkenschloss
https://arxiv.org/abs/2509.04651

Sensitivity-Driven Adaptive Surrogate Modeling for Simulation and Optimization of Dynamical Systems
This paper develops a surrogate model refinement approach for the simulation of dynamical systems and the solution of optimization problems governed by dynamical systems in which surrogates replace expensive-to-compute state- and control-dependent component functions in the dynamics or objective function. For example, trajectory simulation and optimization tasks for an aircraft depend on aerodynamic coefficient functions whose evaluation requires expensive computational fluid dynamics simulatio…

KG-MAS: Knowledge Graph-Enhanced Multi-Agent Infrastructure for coupling physical and digital robotic environments
Walid Abdela
https://arxiv.org/abs/2510.10325 https://

KG-MAS: Knowledge Graph-Enhanced Multi-Agent Infrastructure for coupling physical and digital robotic environments
The seamless integration of physical and digital environments in Cyber-Physical Systems(CPS), particularly within Industry 4.0, presents significant challenges stemming from system heterogeneity and complexity. Traditional approaches often rely on rigid, data-centric solutions like co-simulation frameworks or brittle point-to-point middleware bridges, which lack the semantic richness and flexibility required for intelligent, autonomous coordination. This report introduces the Knowledge Graph-En…

CAPSim: A Fast CPU Performance Simulator Using Attention-based Predictor
Buqing Xu, Jianfeng Zhu, Yichi Zhang, Qinyi Cai, Guanhua Li, Shaojun Wei, Leibo Liu
https://arxiv.org/abs/2510.10484

CAPSim: A Fast CPU Performance Simulator Using Attention-based Predictor
CPU simulators are vital for computer architecture research, primarily for estimating performance under different programs. This poses challenges for fast and accurate simulation of modern CPUs, especially in multi-core systems. Modern CPU peformance simulators such as GEM5 adopt the cycle-accurate and event-driven approach, which is timeconsuming to simulate the extensive microarchitectural behavior of a real benchmark running on out-of-order CPUs. Recently, machine leaning based approach has …

The effect of magnetic fields on vertex reconstructed muon-spin spectroscopy
Pascal Isenring, Zaher Salman
https://arxiv.org/abs/2510.10094 https://arxiv.o…

The effect of magnetic fields on vertex reconstructed muon-spin spectroscopy
The use of a Si pixel-based particle tracking scheme in muSR will, among others, allow measurements using a ten-fold increased stopped muons rate and samples ten times smaller than currently possible. Here we present simulation results to assess the effects of magnetic fields on two spectrometer configurations using a two-layered tracking scheme for the incoming and outgoing particles. At a low magnetic field of up to ~50 mT, the tracking and reconstruction accuracy is only minimally influenced…

Scaling of Magnetic Domain Walls in Perpendicular Magnetic Anisotropy Systems
Guowen Gong, Changmin Xiong, Lijun Zhu
https://arxiv.org/abs/2510.10230 https://

Scaling of Magnetic Domain Walls in Perpendicular Magnetic Anisotropy Systems
Magnetic domain walls play a critical role in the nanoscale evolution of magnetic devices. Despite the early efforts, a complete understanding of the micromagnetic evolution of the width and the type of magnetic domain walls has still remained missing. Here, we report a combined analytical and micromagnetic simulation study and establish the scaling of the magnetic domains as a function of the exchange stiffness (A), uniaxial perpendicular magnetic anisotropy (Ku), saturation magnetization (Ms)…

A Phase-Field Approach to Fracture and Fatigue Analysis: Bridging Theory and Simulation
M. Castill\'on, I. Romero, J. Segurado
https://arxiv.org/abs/2509.08939 https://

A Phase-Field Approach to Fracture and Fatigue Analysis: Bridging Theory and Simulation
This article presents a novel, robust and efficient framework for fatigue crack-propagation that combines the principles of Linear Elastic Fracture Mechanics (LEFM) with phase-field fracture (PFF). Contrary to cycle-by-cycle PFF approaches, this work relies on a single simulation and uses standard crack propagation models such as Paris' law for the material response, simplifying its parametrization. The core of the methodology is the numerical evaluation of the derivative of a specimen's comp…

Modeling Josephson traveling-wave parametric amplifiers with electromagnetic and circuit co-simulation
Likai Yang, Jennifer Wang, Mohamed Awida Hassan, Philip Krantz, Kevin P. O'Brien
https://arxiv.org/abs/2509.07807

Modeling Josephson traveling-wave parametric amplifiers with electromagnetic and circuit co-simulation
Optimizing the performance of cryogenic quantum amplifiers is a key step toward achieving high-fidelity and scalable qubit readout. In this work, we present efficient and accurate modeling of a Josephson traveling-wave parametric amplifier (JTWPA) based on electromagnetic (EM) and circuit co-simulation. In contrast to conventional simulation methods where an equivalent lumped-element circuit model of the JTWPA is required, we directly perform full EM analysis of the device to faithfully determi…

ChemGen: Code Generation for Multispecies Chemical Kinetics in Computational Physics Simulations
Ryan F. Johnson, Eric J. Ching, Ethan S. Genter, Joshua E. Lipman, Andrew D. Kercher, Jay Arcities, Hai Wang
https://arxiv.org/abs/2510.10005

ChemGen: Code Generation for Multispecies Chemical Kinetics in Computational Physics Simulations
This paper introduces ChemGen, a software package that uses code generation to integrate multispecies thermodynamics and chemical kinetics into C+-based computational physics codes. ChemGen aims to make chemical kinetics more accessible in existing simulation frameworks and help bridge the gap between combustion modeling and computational physics. The package employs the concept of decorators which enable flexible C++ code generation to target established software ecosystems. ChemGen generates …

Simulation-based inference via telescoping ratio estimation for trawl processes
Dan Leonte, Rapha\"el Huser, Almut E. D. Veraart
https://arxiv.org/abs/2510.04042 https://…

Simulation-based inference via telescoping ratio estimation for trawl processes
The growing availability of large and complex datasets has increased interest in temporal stochastic processes that can capture stylized facts such as marginal skewness, non-Gaussian tails, long memory, and even non-Markovian dynamics. While such models are often easy to simulate from, parameter estimation remains challenging. Simulation-based inference (SBI) offers a promising way forward, but existing methods typically require large training datasets or complex architectures and frequently yi…

Patriots Clash with Raiders in a Nail-Biting Finish https://www.patriots.com/video/patriots-clash-with-raiders-in-a-nail-biting-finish-madden-26-simulation

Replaced article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- Scalable Evaluation of Online Facilitation Strategies via Synthetic Simulation of Discussions
Dimitris Tsirmpas, Ion Androutsopoulos, John Pavlopoulos

Generalized Multi-agent Social Simulation Framework
Gang Li, Jie Lin, Yining Tang, Ziteng Wang, Yirui Huang, Junyu Zhang, Shuang Luo, Chao Wu, Yike Guo
https://arxiv.org/abs/2510.06225

Generalized Multi-agent Social Simulation Framework
Multi-agent social interaction has clearly benefited from Large Language Models. However, current simulation systems still face challenges such as difficulties in scaling to diverse scenarios and poor reusability due to a lack of modular design. To address these issues, we designed and developed a modular, object-oriented framework that organically integrates various base classes through a hierarchical structure, harvesting scalability and reusability. We inherited the framework to realize comm…

Using AI to Optimize Patient Transfer and Resource Utilization During Mass-Casualty Incidents: A Simulation Platform
Zhaoxun "Lorenz" Liu, Wagner H. Souza, Jay Han, Amin Madani
https://arxiv.org/abs/2509.08756

Using AI to Optimize Patient Transfer and Resource Utilization During Mass-Casualty Incidents: A Simulation Platform
Mass casualty incidents (MCIs) overwhelm healthcare systems and demand rapid, accurate patient-hospital allocation decisions under extreme pressure. Here, we developed and validated a deep reinforcement learning-based decision-support AI agent to optimize patient transfer decisions during simulated MCIs by balancing patient acuity levels, specialized care requirements, hospital capacities, and transport logistics. To integrate this AI agent, we developed MasTER, a web-accessible command dashboa…

Replaced article(s) found for physics.data-an. https://arxiv.org/list/physics.data-an/new
[1/1]:
- DeepOHeat-v1: Efficient Operator Learning for Fast and Trustworthy Thermal Simulation and Optimiz...
Xinling Yu, Ziyue Liu, Hai Li, Yixing Li, Xin Ai, Zhiyu Zeng, Ian Young, …

FIDRS: A Novel Framework for Integrated Distributed Reliable Systems
Mehdi Zekriyapanah Gashti
https://arxiv.org/abs/2510.10833 https://arxiv.org/pdf/2510.…

FIDRS: A Novel Framework for Integrated Distributed Reliable Systems
In this paper we represent a new framework for integrated distributed and reliable systems. In the proposed framework we have used three parts to increase Satisfaction and Performance of this framework. At first we analyze previous frameworks related to integrated systems, then represent new proposed framework in order to improving previous framework, and we discuss its different phases. Finally we compare the results of simulation of the new framework with previous ones. In FIDRS framework, th…

Kazakh oil giant denies cyberattack, says incident was 'planned' phishing drill https://therecord.media/kazakstan-oil-company-kazmunaygas-phishing-simulation-not-cyberattack

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Modeling Low-Temperature Plasmas Simulating Titan's Atmosphere
David Dubois, Alexander W. Raymond, Ella Sciamma-O'Brien, Farid Salama
https://arxiv.org/abs/2509.08063 ht…

Modeling Low-Temperature Plasmas Simulating Titan's Atmosphere
In the study presented here, we model the gas phase chemistry induced by plasma discharge at low temperature (150 K) in the NASA Ames COSmIC Simulation Chamber (COSmIC) using a 1-dimensional multi-fluid plasma model named CO-PRISM (COSmIC Plasma Reactivity and Ionization Simulation Model). Our model incorporates an extensive chemical reaction network to simulate the neutral-neutral and ion-neutral reactions occurring in the COSmIC experiments when using N2-CH4-based gas mixtures relevant to Tit…

SynthDrive: Scalable Real2Sim2Real Sensor Simulation Pipeline for High-Fidelity Asset Generation and Driving Data Synthesis
Zhengqing Chen, Ruohong Mei, Xiaoyang Guo, Qingjie Wang, Yubin Hu, Wei Yin, Weiqiang Ren, Qian Zhang
https://arxiv.org/abs/2509.06798

SynthDrive: Scalable Real2Sim2Real Sensor Simulation Pipeline for High-Fidelity Asset Generation and Driving Data Synthesis
In the field of autonomous driving, sensor simulation is essential for generating rare and diverse scenarios that are difficult to capture in real-world environments. Current solutions fall into two categories: 1) CG-based methods, such as CARLA, which lack diversity and struggle to scale to the vast array of rare cases required for robust perception training; and 2) learning-based approaches, such as NeuSim, which are limited to specific object categories (vehicles) and require extensive multi…

CARJAN: Agent-Based Generation and Simulation of Traffic Scenarios with AJAN
Leonard Frank Neis, Andre Antakli, Matthias Klusch
https://arxiv.org/abs/2508.21411 https://

CARJAN: Agent-Based Generation and Simulation of Traffic Scenarios with AJAN
User-friendly modeling and virtual simulation of urban traffic scenarios with different types of interacting agents such as pedestrians, cyclists and autonomous vehicles remains a challenge. We present CARJAN, a novel tool for semi-automated generation and simulation of such scenarios based on the multi-agent engineering framework AJAN and the driving simulator CARLA. CARJAN provides a visual user interface for the modeling, storage and maintenance of traffic scenario layouts, and leverages SPA…

Simulation of Solar Surface Flux Transport Constrained by Magnetic Power Spectra. I. Flux Transport Parameter
Yukun Luo, Jie Jiang, Ruihui Wang
https://arxiv.org/abs/2509.05989 …

Simulation of Solar Surface Flux Transport Constrained by Magnetic Power Spectra. I. Flux Transport Parameter
The multi-scale structure of the solar surface magnetic field is essential for understanding both the Sun's internal dynamo processes and its external magnetic activity. The surface flux transport (SFT) model has been successful in describing the large-scale evolution of the surface field, but its ability to capture observed multi-scale features, quantified by magnetic power spectra, remains uncertain. Here, we evaluate the SFT model by comparing observed and simulated power spectra across a br…

Port-Hamiltonian Neural Networks: From Theory to Simulation of Interconnected Stochastic Systems
Luca Di Persio, Matthias Ehrhardt, Youness Outaleb, Sofia Rizzotto
https://arxiv.org/abs/2509.06674

Port-Hamiltonian Neural Networks: From Theory to Simulation of Interconnected Stochastic Systems
This work introduces a new framework integrating port-Hamiltonian systems (PHS) and neural network architectures. This framework bridges the gap between deterministic and stochastic modeling of complex dynamical systems. We introduce new mathematical formulations and computational methods that expand the geometric structure of PHS to account for uncertainty, environmental noise, and random perturbations. Building on these advances, we introduce stochastic port-Hamiltonian neural networks (pHNNs…

Replaced article(s) found for math.NA. https://arxiv.org/list/math.NA/new
[2/2]:
- Clean numerical simulation (CNS) of three-dimensional turbulent Kolmogorov flow
Shijie Qin, Shijun Liao

3D Moving-mesh Hydrodynamical Simulations of Wind/Jet Driven Ultraluminous X-ray Source Bubbles
Jiahui Huang, Ken Ohsuga, Hua Feng, Hui Li
https://arxiv.org/abs/2510.09066 https…

3D Moving-mesh Hydrodynamical Simulations of Wind/Jet Driven Ultraluminous X-ray Source Bubbles
We perform 3 dimensional moving-mesh hydrodynamical simulations of bubble nebulae around ultraluminous X-ray sources, using state-of-the-art software AREPO. We use a Monte-Carlo method to inject outflows with uniform mass outflow rate and momentum, in a conical funnel with a specific half opening angle. Simulation results show that the morphology of the bubble is determined by the initial momentum of the outflows, while the mechanical power of the outflows only influences the size of the bubble…

Sensitivity dependence of the Navier-Stokes turbulence of a two-dimensional Rayleigh-B\'{e}nard convection on time-step
Shijie Qin, Kun Xu, Shijun Liao
https://arxiv.org/abs/2510.11220

Sensitivity dependence of the Navier-Stokes turbulence of a two-dimensional Rayleigh-Bénard convection on time-step
A two-dimensional Rayleigh-Bénard convection governed by the Navier-Stokes (NS) equations is solved by traditional direct numerical simulation (DNS) using double precision and various time-steps. It is found that there are two kinds of final flow types, one is vortical flow, the other is zonal flow, and their statistics are completely different. Especially, the two flow types frequently alternate as the time-step decreases to a very small value, suggesting that the time-step corresponding to e…

Simulation-based inference for neutrino interaction model parameter tuning
Karla Tame-Narvaez, Aleksandra \'Ciprijanovi\'c, Steven Gardiner, Giuseppe Cerati
https://arxiv.org/abs/2510.07454

Simulation-based inference for neutrino interaction model parameter tuning
High-energy physics experiments studying neutrinos rely heavily on simulations of their interactions with atomic nuclei. Limitations in the theoretical understanding of these interactions typically necessitate ad hoc tuning of simulation model parameters to data. Traditional tuning methods for neutrino experiments have largely relied on simple algorithms for numerical optimization. While adequate for the modest goals of initial efforts, the complexity of future neutrino tuning campaigns is expe…

The charge-singlet measurement toolbox
Abhijit Chakraborty, Randy Lewis, Christine A. Muschik
https://arxiv.org/abs/2510.08718 https://arxiv.org/pdf/2510.0…

The charge-singlet measurement toolbox
Symmetry is fundamental to physical laws across different scales$\unicode{x2014}$from spacetime structure in general relativity to particle interactions in quantum field theory. Local symmetries, described by gauge theories, are central to phenomena such as superconductivity, topological phases, and the Standard Model of particle physics. Emerging simulation techniques using tensor network states or quantum computers offer exciting new possibilities of exploring the physics of these gauge theor…

DischargeSim: A Simulation Benchmark for Educational Doctor-Patient Communication at Discharge
Zonghai Yao, Michael Sun, Won Seok Jang, Sunjae Kwon, Soie Kwon, Hong Yu
https://arxiv.org/abs/2509.07188 …

DischargeSim: A Simulation Benchmark for Educational Doctor-Patient Communication at Discharge
Discharge communication is a critical yet underexplored component of patient care, where the goal shifts from diagnosis to education. While recent large language model (LLM) benchmarks emphasize in-visit diagnostic reasoning, they fail to evaluate models' ability to support patients after the visit. We introduce DischargeSim, a novel benchmark that evaluates LLMs on their ability to act as personalized discharge educators. DischargeSim simulates post-visit, multi-turn conversations between LLM-…

The chemodynamical memory of a major merger in a NIHAO-UHD Milky Way analogue I: A golden thread through time and space
Sven Buder, Tobias Buck, \'Asa Sk\'ulad\'ottir, Melissa Ness, Madeleine McKenzie, Stephanie Monty
https://arxiv.org/abs/2510.11284

The chemodynamical memory of a major merger in a NIHAO-UHD Milky Way analogue I: A golden thread through time and space
Understanding how the Milky Way's present-day structure was shaped by past major mergers is a key goal of Galactic archaeology. The chemical and dynamical structure of the Galaxy retains the imprint of such events, including a major accretion episode around 8-10 Gyr ago. Recent findings suggest that present-day orbital energy correlates with stellar chemistry and birth location within the merging progenitor galaxy. Using a high-resolution NIHAO-UHD cosmological zoom-in simulation of a Milky Way…

IntersectioNDE: Learning Complex Urban Traffic Dynamics based on Interaction Decoupling Strategy
Enli Lin, Ziyuan Yang, Qiujing Lu, Jianming Hu, Shuo Feng
https://arxiv.org/abs/2510.11534

IntersectioNDE: Learning Complex Urban Traffic Dynamics based on Interaction Decoupling Strategy
Realistic traffic simulation is critical for ensuring the safety and reliability of autonomous vehicles (AVs), especially in complex and diverse urban traffic environments. However, existing data-driven simulators face two key challenges: a limited focus on modeling dense, heterogeneous interactions at urban intersections - which are prevalent, crucial, and practically significant in countries like China, featuring diverse agents including motorized vehicles (MVs), non-motorized vehicles (NMVs)…

An adaptive time-stepping strategy for the modified phase field crystal model with a strong nonlinear vacancy potential
Wanrong Hao, Yunqing Huang
https://arxiv.org/abs/2510.11475

An adaptive time-stepping strategy for the modified phase field crystal model with a strong nonlinear vacancy potential
This paper develops three linear and energy-stable schemes for a modified phase field crystal model with a strong nonlinear vacancy potential (VMPFC model). This sixth-order phase-field model enables realistic crystal growth simulation. Starting from a Crank-Nicolson scheme based on the stabilized-SAV (S-SAV) method, we optimize it via the generalized positive auxiliary variable (GPAV) and modified exponential scalar auxiliary variable (ESAV) methods, thereby reducing computational complexity o…

Special Issue: Commemorating the 110th Anniversary of TANG Au-chin's Birthday Calculation of the Green's function on near-term quantum computers via Cartan decomposition
Lingyun Wan, Jie Liu, Jinlong Yang
https://arxiv.org/abs/2509.09248

Special Issue: Commemorating the 110th Anniversary of TANG Au-chin's Birthday Calculation of the Green's function on near-term quantum computers via Cartan decomposition
Accurate computation of the Green's function is crucial for connecting experimental observations to the underlying quantum states. A major challenge in evaluating the Green's function in the time domain lies in the efficient simulation of quantum state evolution under a given Hamiltonian-a task that becomes exponentially complex for strongly correlated systems on classical computers. Quantum computing provides a promising pathway to overcome this barrier by enabling efficient simulation of quan…

Replaced article(s) found for physics.flu-dyn. https://arxiv.org/list/physics.flu-dyn/new
[1/1]:
- Far-Field Aeroacoustic Shape Optimization Using Large Eddy Simulation
Mohsen Hamedi, Brian Vermeire

Efficient Learning-based Graph Simulation for Temporal Graphs
Sheng Xiang, Chenhao Xu, Dawei Cheng, Xiaoyang Wang, Ying Zhang
https://arxiv.org/abs/2510.05569 https://

Efficient Learning-based Graph Simulation for Temporal Graphs
Graph simulation has recently received a surge of attention in graph processing and analytics. In real-life applications, e.g. social science, biology, and chemistry, many graphs are composed of a series of evolving graphs (i.e., temporal graphs). While most of the existing graph generators focus on static graphs, the temporal information of the graphs is ignored. In this paper, we focus on simulating temporal graphs, which aim to reproduce the structural and temporal properties of the observed…

Atomic bonding in equilibrium single-component melts. The cases of arsenic, antimony and bismuth
Artem A. Tsygankov, Bulat N. Galimzyanov, Anatolii V. Mokshin
https://arxiv.org/abs/2510.10186

Atomic bonding in equilibrium single-component melts. The cases of arsenic, antimony and bismuth
In liquid pnictogens, quasi-stable structures can be formed near melting temperature. The nature of their stability does not have the unified point of view. In the present work, the task of determining the degree of atomic bonding in these structures is solved using the Crystal Orbital Hamilton Population (COHP) method. The original results of ab-initio simulation of arsenic, antimony and bismuth melts near their melting temperatures are used. It is shown that the features of the electron inter…

Phys2Real: Fusing VLM Priors with Interactive Online Adaptation for Uncertainty-Aware Sim-to-Real Manipulation
Maggie Wang, Stephen Tian, Aiden Swann, Ola Shorinwa, Jiajun Wu, Mac Schwager
https://arxiv.org/abs/2510.11689

Phys2Real: Fusing VLM Priors with Interactive Online Adaptation for Uncertainty-Aware Sim-to-Real Manipulation
Learning robotic manipulation policies directly in the real world can be expensive and time-consuming. While reinforcement learning (RL) policies trained in simulation present a scalable alternative, effective sim-to-real transfer remains challenging, particularly for tasks that require precise dynamics. To address this, we propose Phys2Real, a real-to-sim-to-real RL pipeline that combines vision-language model (VLM)-inferred physical parameter estimates with interactive adaptation through unce…

M&SCheck: Towards a Checklist to Support Software Engineering Newcomers to the Modeling and Simulation Area
Luiza Martins de Freitas Cintra, Philipp Zech, Mohamad Kassab, Eliomar Ara\'ujo Lima, Sofia Larissa da Costa Paiva, Valdemar Vicente Graciano Neto
https://arxiv.org/abs/2509.25625

M&SCheck: Towards a Checklist to Support Software Engineering Newcomers to the Modeling and Simulation Area
The advent of increasingly complex and dynamic ecosystems, such as digital twins (DT), smart cities and Industry 4.0 and 5.0, has made evident the need to include modeling and simulation (M&S) in the software development life cycle. Such disruptive systems include simulation models in their own architecture (such as DT) or require the use of simulation models to represent the high degree of movement and the multiplicity of interactions that occur between the involved systems. However, when soft…

Multi-modal Foundation Model for Cosmological Simulation Data
Bin Xia, Nesar Ramachandra, Azton I. Wells, Salman Habib, John Wise
https://arxiv.org/abs/2510.07684 https://

Multi-modal Foundation Model for Cosmological Simulation Data
We present a multi-modal foundation model for astrophysical galaxy data, designed to map between simulation- and observation-based galactic features. Our encoder-only transformer flexibly ingests scalar quantities (e.g., redshifts, galaxy masses) and vectors (e.g., star formation histories, spectra), supporting multi-task training that includes within-modality reconstruction and cross-modality prediction. With a dynamic masking strategy, the model can query arbitrary galaxy properties from part…

Simulation of one and two qubit superconducting quantum gates under the non-Markovian $1/f$ noise
Yinjia Chen, Shuocang Zhang, Qiang Shi
https://arxiv.org/abs/2509.07693 https:/…

Simulation of one and two qubit superconducting quantum gates under the non-Markovian $1/f$ noise
Non-Markovian $1/f$ noise consists a dominant source of decoherence in superconducting qubits, yet its slow nature poses a significant challenge for accurate simulation. Here we develop a hierarchical equations of motion (HEOM) framework that enables efficient and reliable modeling of qubit dynamics and gate operations under $1/f$ noise. By using the approach, it is first shown that perturbative quantum master equations may fail to reproduce the correct dephasing dynamics of a qubit coupled to …

Placeit! A Framework for Learning Robot Object Placement Skills
Amina Ferrad, Johann Huber, Fran\c{c}ois H\'el\'enon, Julien Gleyze, Mahdi Khoramshahi, St\'ephane Doncieux
https://arxiv.org/abs/2510.09267

Placeit! A Framework for Learning Robot Object Placement Skills
Robotics research has made significant strides in learning, yet mastering basic skills like object placement remains a fundamental challenge. A key bottleneck is the acquisition of large-scale, high-quality data, which is often a manual and laborious process. Inspired by Graspit!, a foundational work that used simulation to automatically generate dexterous grasp poses, we introduce Placeit!, an evolutionary-computation framework for generating valid placement positions for rigid objects. Placei…

AuraSight: Generating Realistic Social Media Data
Lynnette Hui Xian Ng, Bianca N. Y. Kang, Kathleen M. Carley
https://arxiv.org/abs/2509.08927 https://arxi…

AuraSight: Generating Realistic Social Media Data
This document details the narrative and technical design behind the process of generating a quasi-realistic set X data for a fictional multi-day pop culture episode (AuraSight). Social media post simulation is essential towards creating realistic training scenarios for understanding emergent network behavior that formed from known sets of agents. Our social media post generation pipeline uses the AESOP-SynSM engine, which employs a hybrid approach of agent-based and generative artificial intell…

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A high-fidelity and efficient framework for point-particle direct numerical simulation based on multi-block overset grids
Taiyang Wang, Baoqing Meng, Baolin Tian, Yaomin Zhao
https://arxiv.org/abs/2509.07443

A high-fidelity and efficient framework for point-particle direct numerical simulation based on multi-block overset grids
In this work, we present a high-fidelity and efficient point-particle direct numerical simulation framework based on a multi-block overset curvilinear grid system, enabling large-scale Lagrangian particle tracking in complex geometries with high-order accuracy and low computational cost. To handle the multi-domain topological challenges inherent in such configurations, we develop an efficient particle storage and redistribution framework leveraging overset grid techniques. In particular, two op…

Nonlinear Co-simulation for Designing Kinetic Inductance Parametric Amplifiers
Likai Yang, Yufeng Wu, Chaofan Wang, Mingrui Xu, Hong X. Tang, Mohamed A. Hassan, Eric T. Holland
https://arxiv.org/abs/2509.07816

Nonlinear Co-simulation for Designing Kinetic Inductance Parametric Amplifiers
Kinetic inductance parametric amplifiers (KIPAs) have been widely studied for small-signal detection in superconducting quantum circuits. In this work, we demonstrate the modeling of a niobium nitride nanowire based KIPA using electromagnetic (EM) and circuit co-simulation, and compare the outcomes with experimental results. EM analysis is first performed on the device layout, taking into account the linear part of the kinetic inductance. The results are then integrated into a harmonic balance …

An Integrated Open Source Software System for the Generation and Analysis of Subject-Specific Blood Flow Simulation Ensembles
Simon Leistikow, Thomas Miro, Adrian Kummerl\"ander, Ali Nahardani, Katja Gr\"un, Markus Franz, Verena Hoerr, Mathias J. Krause, Lars Linsen
https://arxiv.org/abs/2509.09392

An Integrated Open Source Software System for the Generation and Analysis of Subject-Specific Blood Flow Simulation Ensembles
Background and Objective: Hemodynamic analysis of blood flow through arteries and veins is critical for diagnosing cardiovascular diseases, such as aneurysms and stenoses, and for investigating cardiovascular parameters, such as turbulence and wall shear stress. For subject-specific analyses, the anatomy and blood flow of the subject can be captured non-invasively using structural and 4D Magnetic Resonance Imaging (MRI). Computational Fluid Dynamics (CFD), on the other hand, can be used to gene…

Performance Characterization of a Point-Cloud-Based Path Planner in Off-Road Terrain
Casey D. Majhor, Jeremy P. Bos
https://arxiv.org/abs/2509.07321 https://

Performance Characterization of a Point-Cloud-Based Path Planner in Off-Road Terrain
We present a comprehensive evaluation of a point-cloud-based navigation stack, MUONS, for autonomous off-road navigation. Performance is characterized by analyzing the results of 30,000 planning and navigation trials in simulation and validated through field testing. Our simulation campaign considers three kinematically challenging terrain maps and twenty combinations of seven path-planning parameters. In simulation, our MUONS-equipped AGV achieved a 0.98 success rate and experienced no failure…

High-order Magnus Expansion for Hamiltonian Simulation
Di Fang, Diyi Liu, Shuchen Zhu
https://arxiv.org/abs/2509.06054 https://arxiv.org/pdf/2509.06054

High-order Magnus Expansion for Hamiltonian Simulation
Efficient simulation of quantum dynamics with time-dependent Hamiltonians is important not only for time-varying systems but also for time-independent Hamiltonians in the interaction picture. Such simulations are more challenging than their time-independent counterparts due to the complexity introduced by time ordering. Existing algorithms that aim to capture commutator-based scaling either exhibit polynomial cost dependence on the Hamiltonian's time derivatives or are limited to low-order accu…

Tumor Obliteration by Resonant Amplification (TOR) A Nonthermal, Spectrally-Targeted Approach to Cancer Disintegration
Cesar Mello, Fernando Medina da Cunha
https://arxiv.org/abs/2510.09982

Tumor Obliteration by Resonant Amplification (TOR) A Nonthermal, Spectrally-Targeted Approach to Cancer Disintegration
Tumor Obliteration by Resonant Amplification (TOR) was evaluated purely in simulation. Forward models in COMSOL, ANSYS, and ABAQUS used the same small-strain rheology, nonthermal/noncavitational limits, and an emulated closed loop (phase-locked actuation plus contrast/safety gating). Over >= 200 Monte Carlo runs per setup, TOR produced per-focus extinction in 2.6-3.2 s at approx. 0.85-0.90 J/cm^3, with selectivity Q = 39 +/- 5, peak temperature rise dT_max <= 0.2 deg C, and CEM43 << 1, consiste…

Utility-Scale Quantum State Preparation: Classical Training using Pauli Path Simulation
Cheng-Ju Lin, Hrant Gharibyan, Vincent P. Su
https://arxiv.org/abs/2510.02428 https://

Utility-Scale Quantum State Preparation: Classical Training using Pauli Path Simulation
We use Pauli Path simulation to variationally obtain parametrized circuits for preparing ground states of various quantum many-body Hamiltonians. These include the quantum Ising model in one dimension, in two dimensions on square and heavy-hex lattices, and the Kitaev honeycomb model, all at system sizes of one hundred qubits or more, beyond the reach of exact state-vector simulation, thereby reaching utility scale. We benchmark the Pauli Path simulation results against exact ground-state energ…

Scalings and simulation requirements in two-phase flows
Luis H. Hatashita, Pranav Nathan, Suhas S. Jain
https://arxiv.org/abs/2510.07727 https://arxiv.org/…

Scalings and simulation requirements in two-phase flows
In this work, important two-phase flow scalings are derived, which enable the quantification of grid-point and time-step requirements as functions of Re, We, and Ca numbers. The adequate grid resolution is determined in the inertia-dominated regime with the aid of high-fidelity simulations of stationary two-phase homogeneous isotropic turbulence by evaluating convergence of total interfacial area, size distribution, SMD, and curvature distribution. Although standards for DNS for single-phase tu…

Wavefunction Flows: Efficient Quantum Simulation of Continuous Flow Models
David Layden, Ryan Sweke, Vojt\v{e}ch Havl\'i\v{c}ek, Anirban Chowdhury, Kirill Neklyudov
https://arxiv.org/abs/2510.08462

Wavefunction Flows: Efficient Quantum Simulation of Continuous Flow Models
Flow models are a cornerstone of modern machine learning. They are generative models that progressively transform probability distributions according to learned dynamics. Specifically, they learn a continuous-time Markov process that efficiently maps samples from a simple source distribution into samples from a complex target distribution. We show that these models are naturally related to the Schrödinger equation, for an unusual Hamiltonian on continuous variables. Moreover, we prove that the…

Scalable parallel simulation of quantum circuits on CPU and GPU systems
Guolong Zhong, Yi Fan, Zhenyu Li
https://arxiv.org/abs/2509.04955 https://arxiv.org…

Scalable parallel simulation of quantum circuits on CPU and GPU systems
Quantum computing enables parallelism through superposition and entanglement and offers advantages over classical computing architectures. However, due to the limitations of current quantum hardware in the noisy intermediate-scale quantum (NISQ) era, classical simulation remains a critical tool for developing quantum algorithms. In this research, we present a comprehensive parallelization solution for the Q$^2$Chemistry software package, delivering significant performance improvements for the f…

Stability of digital and analog quantum simulations under noise
Jayant Rao, Jens Eisert, Tommaso Guaita
https://arxiv.org/abs/2510.08467 https://arxiv.org/…

Stability of digital and analog quantum simulations under noise
Quantum simulation is a central application of near-term quantum devices, pursued in both analog and digital architectures. A key challenge for both paradigms is the effect of imperfections and noise on predictive power. In this work, we present a rigorous and physically transparent comparison of the stability of digital and analog quantum simulators under a variety of perturbative noise models. We provide rigorous worst- and average-case error bounds for noisy quantum simulation of local obser…

Subspace Variational Quantum Simulation: Fidelity Lower Bounds as Measures of Training Success
Seung Park, Dongkeun Lee, Jeongho Bang, Hoon Ryu, Kyunghyun Baek
https://arxiv.org/abs/2509.06360

Subspace Variational Quantum Simulation: Fidelity Lower Bounds as Measures of Training Success
We propose an iterative variational quantum algorithm to simulate the time evolution of arbitrary initial states within a given subspace. The algorithm compresses the Trotter circuit into a shorter-depth parameterized circuit, which is optimized simultaneously over multiple initial states in a single training process using fidelity-based cost functions. After the whole training procedure, we provide an efficiently computable lower bound on the fidelities for arbitrary states within the subspace…

Hybrid quantum-classical analog simulation of two-dimensional Fermi-Hubbard models with neutral atoms
Sergi Juli\`a-Farr\'e, Antoine Michel, Christophe Domain, Joseph Mikael, Jacques-Charles Lafoucriere, Joseph Vovrosh, Ahmed Chahlaoui, Dorian Claveau, Guillaume Villaret, Julius de Hond, Lo\"ic Henriet, Antoine Browaeys, Thomas Ayral, Alexandre Dauphin

Hybrid quantum-classical analog simulation of two-dimensional Fermi-Hubbard models with neutral atoms
We experimentally study the two-dimensional Fermi-Hubbard model using a Rydberg-based quantum processing unit in the analog mode. Our approach avoids encoding directly the original fermions into qubits and instead relies on reformulating the original model onto a system of fermions coupled to spins and then decoupling them in a self-consistent manner. We then introduce the auxiliary spin solver: this hybrid quantum-classical algorithm handles a free-fermion problem, which can be solved efficien…