#simulation

24 curated events

papersTODAY 04:00 UTC

Study questions realism of language-model agents in farming decision simulations

A new arXiv paper examines whether language-model agents can credibly stand in for human respondents in surveys and social simulations. The authors argue that judging realism from population averages or distributional similarity can be misleading, an effect they call the "average-farmer illusion." Their experiments test what such aggregate evidence actually demonstrates about individual-level behavior.

agents/simulation/evaluation/research

papersTODAY 04:00 UTC

Method Turns Sequenced Fuzzy Cognitive Maps into Causal Virtual Worlds via Video Generators

A new arXiv paper describes an approach for building and steering causal virtual worlds using large language and video model agents. It relies on feedback fuzzy cognitive maps to capture the detailed causal structure of the simulated environment. The technique converts sequenced FCMs into worlds that video generators can render.

research/video-generation/agents/simulation

papersTODAY 04:00 UTC

Simulation-based inference used to calibrate ocean model mixing parameters

An arXiv preprint describes a method for tuning the free coefficients in vertical mixing parametrizations of single-column ocean models. Because these coefficients cannot be measured directly, the authors calibrate them against high-fidelity references such as large-eddy simulations using simulation-based inference. The approach is presented as an alternative to existing techniques that yield only point estimates.

research/simulation/ocean-modeling/calibration

papersTODAY 04:00 UTC

Study Tests Whether LLMs Can Simulate Individual Financial Decisions

A preliminary arXiv paper examines whether large language models can stand in for people as user simulators in financial settings. The authors ran a controlled paper-trading experiment with 120 volunteers to see how well model behavior tracks evolving individual investing choices. They conclude that current LLM simulation of such decisions is not yet reliable and call for further work.

llm/simulation/finance/research

papersTODAY 04:00 UTC

GzDRL: Single-process deep reinforcement learning framework for Gazebo

Researchers introduce GzDRL, a reinforcement learning framework that runs Gazebo robotics simulations in a single process rather than relying on conventional middleware-based bridges. The authors say this design targets long-standing obstacles to reproducible and scalable robotics experimentation. The work is posted as an arXiv preprint (2609.13243).

robotics/reinforcement-learning/simulation/reproducibility

papersTODAY 04:00 UTC

Attention-Based Flux Scheme Targets Spurious Oscillations in Finite-Volume Solvers

A new arXiv preprint proposes a conservative finite-volume method on fixed grids in which an attention mechanism selects upstream information to build the numerical flux. The scheme is conditioned on the CFL constraint so that a shock can propagate across multiple cells in a single update without being smeared or broken. The work sits at the intersection of numerical PDE solvers and machine learning attention architectures.

attention/numerical-methods/simulation/arxiv

papersTODAY 04:00 UTC

Study Tests Whether SNOWPACK Simulations Can Predict Satellite-Mapped Avalanches

Researchers examined whether simulated snowpack conditions from the SNOWPACK model can serve as a data-driven predictor of avalanche activity detected by satellite imagery. The work targets regions where direct field observations are too sparse to support conventional forecasting. It points toward filling observational gaps in mountain avalanche monitoring with model-based estimates.

research/remote-sensing/forecasting/simulation

papersTODAY 04:00 UTC

PINN framework models blood flow dynamics in abdominal aortic aneurysms

Researchers built a three-dimensional physics-informed neural network to simulate pulsatile blood flow in the human aorta, focusing on abdominal aortic aneurysm haemodynamics. The approach embeds physical laws into the training process, allowing time-resolved simulation without conventional mesh-based solvers. The work is a preprint posted to arXiv.

research/physics-informed/healthcare/simulation

papersTODAY 04:00 UTC

arXiv paper introduces agentic framework for high-throughput atomistic simulations

A new arXiv preprint describes a system that uses autonomous agents to run machine-learning interatomic potential simulations at scale. The work targets the difficulty of applying these potentials across broad chemical spaces, where near-ab initio accuracy is achieved at much lower computational cost. The abstract frames the effort as addressing a central bottleneck in practical use of such potentials.

agents/machine-learning/simulation/materials-science

papersTODAY 04:00 UTC

ModiGen: LLM Workflow for Multi-Task Modelica Code Generation

A new arXiv paper introduces ModiGen, a large language model-driven workflow designed to handle several Modelica code generation tasks at once. Modelica is used to simulate complex physical systems, but building and tuning such models typically demands deep domain knowledge. The work targets lowering that barrier by having LLMs assist with code creation for these simulation models.

llm/code-generation/modelica/simulation

papersTODAY 04:00 UTC

Hybrid CNN-Cellular Automata Model Aims to Improve Aerial Wildfire Suppression Planning

Researchers present a framework for deciding when, where, and how to deploy limited firefighting aircraft during wildfire suppression. The approach pairs a frozen hybrid convolutional neural network with a cellular automaton simulator to model fire spread and test intervention strategies. It falls in the category of applied machine learning research rather than a released product.

research/simulation/cnn/wildfire

papersTODAY 04:00 UTC

Multifidelity TDNN and Physics-Informed Residual Learning for Railway Bogie Prediction

A revised arXiv preprint proposes a hybrid approach for predicting railway bogie responses, combining a time-delay neural network with physics-informed residual learning across simulations of differing fidelity. The method targets operating conditions that are impractical to test exhaustively, using agreement with representative measurements as validation evidence. It sits within ongoing work on surrogate modeling for engineering simulation.

railways/simulation/physics-informed/neural-networks

papersTODAY 04:00 UTC

Physics-Constrained Neural Surrogate Models Domain Growth Under Conserved Kinetics

A revised arXiv preprint presents a neural surrogate model that incorporates physical constraints to predict how domains grow in systems governed by conserved kinetics. Such systems are typically described by nonlinear partial differential equations, which are costly to solve, and learned surrogates aim to offer faster alternatives. The work targets physics, chemistry, and biology applications where accurate spatiotemporal prediction matters.

research/neural-surrogates/physics-informed/simulation

papersTODAY 04:00 UTC

Online Reinforcement Learning Applied Inside Met Office Unified Model

A study from the Met Office pairs its Unified Model with a distributed reinforcement-learning setup so that agents can be trained while the numerical weather model runs. The work targets machine-learned corrections that stay stable as the underlying forecast model evolves, rather than being trained offline on frozen data. The authors describe the coupling architecture that lets model and agent processes communicate across distributed infrastructure.

reinforcement-learning/weather-forecasting/simulation/distributed

papersTODAY 04:00 UTC

arXiv paper models bow control on a measured bowed-string model

A new arXiv preprint presents a finite-difference bowed-string simulation that resolves Stribeck friction implicitly, alongside a diagnostic for playing regimes and Schelleng bow-force limits computed for four strings. It also revisits a minimum-bow-force law and compares a recurrent learned controller against a supervision ceiling that constrains how much imitation can help.

arxiv/physics/control/simulation

papersSEP 11 04:00 UTC

arXiv paper targets gap between a priori and a posteriori accuracy in neural network subgrid stress models

A revised arXiv preprint examines why neural network subgrid stress models perform well in a priori tests but deteriorate in a posteriori large eddy simulations. The authors propose approaches to reduce this discrepancy so that model evaluation better reflects real simulation behavior. The work sits in computational fluid dynamics research rather than commercial AI deployment.

research/simulation/neural-networks/fluid-dynamics

papersSEP 10 04:00 UTC

Hi-FLoop: Hierarchical State-Feedback Loops for Multi-Timescale World Modeling

A new paper introduces Hi-FLoop, a hierarchical architecture that uses state-feedback loops to model traffic scenes across multiple decision timescales. The method targets long-horizon, closed-loop multi-agent simulation, aiming to produce coordinated and physically plausible rollouts from road maps and past agent behavior.

research/world-models/simulation/multi-agent

papersSEP 10 04:00 UTC

Framework simulates patients to assess risks of conversational healthcare AI decision aids

Researchers have developed and validated a patient simulation framework designed to probe conversational healthcare AI systems for potential harms before deployment. The approach maps to the NIST AI Risk Management Framework's MAP and MEASURE functions and was demonstrated by testing an antidepressant decision-support assistant. According to the authors, it provides an empirical basis for identifying and measuring risks in clinical AI tools.

healthcare/safety/evaluation/simulation

papersSEP 10 04:00 UTC

Probabilistic Real2Sim2Real approach improves vision-driven deformable linear object manipulation

A new research paper applies likelihood-free inference to real2sim2real transfer for manipulating deformable linear objects such as cables using vision. By estimating a distribution over simulation parameters from black-box models, the method handles nonlinear and stochastic dynamics that are hard to model directly. A posterior-driven heuristic then adapts the inferred parameter support so control policies can generalize to varied deployment conditions.

robotics/simulation/inference/manipulation

papersSEP 10 04:00 UTC

ML models with different inductive biases tested for cosmological inference from galaxy catalogs

Researchers used simulated galaxy catalogs from the CAMELS hydrodynamic simulations to infer the matter density parameter with machine learning models that embody different inductive biases. The paper evaluates how these architectural assumptions shape field-level likelihood-free inference of cosmological parameters.

cosmology/machine-learning/simulation/inference

papersSEP 10 04:00 UTC

Deep Learning-FEM Approach Links Extruded Filament Shape to Buildability in 3D Concrete Printing

Researchers present a combined deep learning and finite element framework that factors in the real cross-sectional geometry of extruded concrete filaments when evaluating whether printed layers can bear the weight of subsequent ones. The work addresses a common limitation of existing buildability assessments, which typically rely on simplified filament shapes, potentially misjudging the stability of 3D-printed concrete structures.

deep-learning/3d-printing/construction/simulation

papersSEP 10 04:00 UTC

Graph Neural Operator Surrogate Predicts Stress Tensor Fields in Concrete Penetration

Researchers have developed a graph neural operator that estimates full mid-plane stress tensor fields in concrete during projectile penetration, connecting the material's mesoscale structure to complete field-level predictions. The surrogate was trained on data from a detailed aggregate-resolved LS-DYNA simulation and is designed to generalize across different impact velocities, offering a faster stand-in for expensive finite-element computations.

graph-neural-networks/surrogate-models/simulation/materials-science

papersSEP 10 04:00 UTC

Study Probes How Defendant Statements Influence LLM-Simulated Jurors

A new paper examines whether and how a defendant's courtroom statement shifts the verdicts of large language models simulating jurors in common-law trials. The authors focus on persuasion effects and potential bias in these simulated decisions, a setting where LLM-based modeling of human judgment had not previously been studied.

bias/evaluation/simulation/legal

papersSEP 12 04:00 UTC

Self-Evolving LLM Agent Accumulates Skills for Urban Traffic Simulation

Researchers introduce SimSkill, an agent that draws on the idea of cumulative culture to retain and build on skills and knowledge over repeated experiences. The system is designed for the Simulation of Urban MObility (SUMO) traffic environment, where it evolves its own capabilities rather than relying on fixed, hand-crafted policies. A revised version of the preprint is now available on arXiv.

agents/llm-agents/simulation/research