Multiphysics Simulation

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18 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Torch-DPhys: A Torch-Native, MOOSE-Compatible Framework for Differentiable Physics Simulation and Hybrid Machine Learning Integration
Oct 16, 2026Akzhol Almukhametov, Yang LiuDifferentiable Physics Physics-Based Simulation
Department of Nuclear Engineering, Texas A&M University
LLM-Assisted Generation of Transparent, Open-Source Multiphysics Models of Electrochemical Devices
Oct 7, 2026Sebastian Castro, Maya F. Schuchert, Spencer A. McCluskey +2Multiphysics Simulation LLM Code Generation
Department of Chemical and Biomolecular Engineering, New York University, Brooklyn, NY, USA · Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, BC, Canada
Asking the World: Generalist Physical Reasoning through Agentic World Modeling and Probing
Sep 30, 2026Shenxiang Zeng, Chen Yang, Peiyao Chen +3Multiphysics Simulation World Model Learning
Tsinghua University
When a neural surrogate cannot accelerate a solver: runtime share, closed-loop drift, and the economics of uncertainty gating in a stiff coupled simulation
Aug 24, 2026L. Thümmler, T. KurodaNeural Surrogate Modeling PDE Surrogate Modeling
Department of Mathematics, ETH Zürich, Rämistrasse 101, 8092 Zürich, Switzerland · Max Planck Institute for Gravitational Physics (Albert Einstein Institute), Am Mühlenberg 1, D-14476 Potsdam, Germany
Deploying Frontier Agentic Technology in MOOSEnger, a Multiphysics-Capable AI Assistant
Aug 16, 2026Zaid Abulawi, Mengnan Li, Guillaume Giudicelli +2Tool-Augmented Language Model Agents LLM Agent Harnesses
Department of Nuclear Engineering, Texas A&M University (TAMU), TX, USA · Idaho National Laboratory (INL), ID, USA
A Constrained Natural-Language Interface for Variational Multi-Physics Finite Element Simulations in FEniCS
Jun 9, 2026Nilay Upadhyay, Wesley F. ReinhartMultiphysics Simulation PDE Solving
Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, PA 16802, USA · Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USA
Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization
May 22, 2026Youngjae Park, Jaemin Kim, Junghwa HongGauss-Newton Optimization Neural Network Optimization
Dept. of Control and Instrumentation Engineering, Korea University, Sejong, South Korea · BK21 FOUR Smart Mobility Education and Research Team, Korea University, Sejong, South Korea
pyforce-1.0.0: Python Framework for data-driven model Order Reduction of multi-physiCs problEms
May 18, 2026Stefano Riva, Yantao Luo, Carolina Introini +1Multiphysics Simulation Reduced-Order Modeling
Department of Energy, Nuclear Engineering Division, Politecnico di Milano, Milan, Italy · Department of Mechanical and Nuclear Engineering and Emirates Nuclear Technology Center, Khalifa University, Abu Dhabi (127788), United Arab Emirates
WorldParticle: Unified World Simulation of Lagrangian Particle Dynamics via Transformer
May 14, 2026Caoliwen Wang, Minghao Guo, Siyuan Chen +13Neural Surrogate Modeling Transformer
University of British Columbia, Canada · MIT CSAIL, USA · Georgia Institute of Technology, USA +7
Your Simulation Runs but Solves the Wrong Physics: PDE-Grounded Intent Verification for LLM-Generated Multiphysics Simulation Code
May 10, 2026Zhenghan Song, Yulong Liu, Cheng Wan +4LLM Self-Correction Scientific Code Generation
Cornell University · Columbia University · Harvard University & Nanyang Technological University
Variational Matrix-Learning Fourier Networks for Parametric Multiphysics Surrogates
May 4, 2026Xinyu Li, Jianhua Zhang, Liang ChenMultiphysics Simulation PDE Surrogate Modeling
School of Microelectronics, Shanghai University, Shanghai 201800, China
Differentiable Multiphysics Co-Optimization via Implicit Neural Representations: A Transient Hamburger-Cooking Benchmark
May 1, 2026Navid ZobeiryDifferentiable Optimization Multi-Objective Optimization
Department of Materials Science and Engineering, University of Washington · William E. Boeing Department of Aeronautics & Astronautics, University of Washington, Seattle, WA 98195, USA
PI-JEPA: Label-Free Surrogate Pretraining for Coupled Multiphysics Simulation via Operator-Split Latent Prediction
Apr 1, 2026Brandon Yee, Pairie KohMultiphysics Simulation PDE Surrogate Modeling
Physics Lab, Yee Collins Research Group · Graduate School of Business, Stanford University
AutoMOOSE: An Agentic AI for Autonomous Phase-Field Simulation
Mar 22, 2026Sukriti Manna, Henry Chan, Subramanian K. R. S. SankaranarayananComputational Materials Science AI Agents for Scientific Discovery
Department of Mechanical and Industrial Engineering, University of Illinois Chicago, Chicago, IL 60607, USA · Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL 60439, USA
A scalable neural bundle map for multiphysics prediction in lithium-ion battery across varying configurations
Mar 17, 2026Zhiwei Zhao, Changqing Liu, Jie Lin +4Multiphysics Simulation Neural Surrogate Modeling
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China · School of Mechanical and Aerospace Engineering, Queen's University Belfast, Belfast BT9 5AH, UK
Tackling multiphysics problems via finite element-guided physics-informed operator learning
Mar 2, 2026Yusuke Yamazaki, Reza Najian Asl, Markus Apel +2Multiphysics Simulation Physics-Informed Neural Operators
Graduate School of Science and Technology, Keio University, Hiyoshi3-14-1,Kohoku-ku, Yokohama 223-8522, Japan · Chair of Structural Analysis, Technical University of Munich, Arcisstraße 21, 80333 Munich, Germany · ACCESS e.V., Intzestr. 5, 52072 Aachen, Germany +1
A Unified Physics-Informed Neural Network for Modeling Coupled Electro- and Elastodynamic Wave Propagation Using Three-Stage Loss Optimization
Feb 14, 2026Suhas Suresh Bharadwaj, Reuben Thomas ThovelilNeural PDE Solvers PDE Solving
Department of Electrical and Electronics Engineering, BITS Pilani, Dubai Campus, Dubai, United Arab Emirates
Multi-Agent Design Assistant for the Simulation of Inertial Fusion Energy
Oct 2, 2025Meir H. Shachar, Dane M. Sterbentz, Harshitha Menon +10Multiphysics Simulation Multi-Agent LLM Systems
Lawrence Livermore National Laboratory Livermore, CA, USA · Lawrence Livermore National Laboratory Livermore, CA 94550, USA · Sandia National Laboratories Albuquerque, NM, USA +3

Oct 16, 2026J/K move · Enter open · S save

Torch-DPhys: A Torch-Native, MOOSE-Compatible Framework for Differentiable Physics Simulation and Hybrid Machine Learning Integration

Akzhol Almukhametov, Yang Liu

Department of Nuclear Engineering, Texas A&M University

Differentiable Physics Physics-Based Simulation Multiphysics Simulation

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We present Torch-DPhys, a Torch-native framework implemented in PyTorch for differentiable physics simulation and hybrid physics-machine learning. It implements selected thermal-hydraulic modeling concepts and discretizations from MOOSE's Thermal Hydraulics Module and the System Analysis Module (SAM), with a preliminary extension to continuum mechanics. Native tensor residuals support CPU and GPU execution, compilation of selected numerical kernels, and sparse direct linear solves. Sensitivities are obtained by differentiating executed numerical operations or through discrete adjoints of converged residual equations. Verification combines analytical solutions, reference-code comparisons, and derivative checks. Demonstrations cover plane-strain elasticity, one- and two-loop thermal-hydraulic transients, and an axisymmetric molten-salt fast reactor. For the reactor, comparison with SAM-Lite gives a maximum normalized field discrepancy of 1.13 x 10^-5 over 200 adaptive time steps, demonstrating agreement between the tested discrete implementations. Selected transient sensitivities are assessed using tangent-linear calculations and finite differences on fixed time schedules. Two applications illustrate the framework's connection between simulation and optimization. A residual-adjoint parameter-identification calculation recovers nominal pump head and heating power with relative errors below 0.06% from noiseless synthetic MOOSE observations. A separate three-loop plant demonstration uses solver-in-the-loop training of a predictive controller by differentiating trajectory-level objectives through the thermal-hydraulic simulation. These examples illustrate a foundation for gradient-based inference, control, and coupled physics-machine learning models in nuclear engineering.