Image de couverture de livre

Differentiable Physics-Neural Models enable Learning of Non-Markovian Closures for Accelerated Coarse-Grained Physics Simulations

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Xue, Tingkai, Ooi, Chin Chun, Ge, Zhengwei, Leong, Fong Yew, Li, Hongying, Kang, Chang Wei
Format:	Preprint
Publié:	2025
Sujets:	Computational Physics Machine Learning Fluid Dynamics
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Hybrid Quantum Physics-informed Neural Network: Towards Efficient Learning of High-speed Flows
par: Leong, Fong Yew, et autres
Publié: (2025)

NeuralFVM: Neural-physics-based Finite Volume Method for Turbulent Flows Using the $k$-$ω$ Model
par: Xue, Tingkai, et autres
Publié: (2026)

Physics-Constrained Neural Closure for Lattice Boltzmann Large-Eddy Simulation
par: Khan, Muhammad Idrees, et autres
Publié: (2026)

Two-point Turbulence Closures in Physical Space
par: Zambrano, Noah, et autres
Publié: (2025)

Neural-Network-based Viscosity Closure for Non-Newtonian Multiphase Flows
par: Murugaiyan, Suresh, et autres
Publié: (2026)

CompARE: A Computational framework for Airborne Respiratory disease Evaluation integrating flow physics and human behavior
par: Leong, Fong Yew, et autres
Publié: (2025)

JAX-Shock: A Differentiable, GPU-Accelerated, Shock-Capturing Neural Solver for Compressible Flow Simulation
par: Zhang, Bo
Publié: (2026)

Physics Informed Neural Network-based Computational Method for Accelerating Time-Periodic Unsteady CFD Simulations
par: Chaplot, Lakshya, et autres
Publié: (2026)

Learning Differentiable Weak-Form Corrections to Accelerate Finite Element Simulations
par: Jung, Junoh, et autres
Publié: (2026)

A Meshless Solver for Blood Flow Simulations in Elastic Vessels Using Physics-Informed Neural Network
par: Zhang, Han, et autres
Publié: (2023)

Turbulence and Rossby Wave Dynamics with Realizable Eddy Damped Markovian Anisotropic Closure
par: Frederiksen, Jorgen S., et autres
Publié: (2024)

Investigation of Numerical Diffusion in Aerodynamic Flow Simulations with Physics Informed Neural Networks
par: Warey, Alok, et autres
Publié: (2021)

Physics-Informed Neural Networks for the Korteweg-de Vries Equation for Internal Solitary Wave Problem: Forward Simulation and Inverse Parameter Estimation
par: Kang, Ming, et autres
Publié: (2025)

Quantifying Model Uncertainty of Neural Network-based Turbulence Closures
par: Grogan, Cody, et autres
Publié: (2024)

Coarse-graining Algorithms for the Eulerian-Lagrangian Simulation of Particle-laden Flows
par: Eshraghi, H., et autres
Publié: (2025)

Neural-Network Closures for Complex-Shaped Particles in the Force-Coupling Method
par: Laudato, Marco
Publié: (2025)

Non-intrusive Learning of Physics-Informed Spatio-temporal Surrogate for Accelerating Design
par: Mondal, Sudeepta, et autres
Publié: (2026)

A Central Differential Flux with High-Order Dissipation for Robust Simulations of Transcritical Flows
par: Xu, Bonan, et autres
Publié: (2025)

Extraction of Moment Closures for Strongly Non-Equilibrium Flows via Machine Learning
par: Song, Hang, et autres
Publié: (2025)

Physics-Informed Chebyshev Polynomial Neural Operator for Parametric Partial Differential Equations
par: Chen, Biao, et autres
Publié: (2026)

On the Non-Markovian Navier-Stokes Framework for Turbulence Modeling -- A Preliminary Analysis
par: Hannani, Siamak Kazemzadeh, et autres
Publié: (2025)

MeshMask: Physics-Based Simulations with Masked Graph Neural Networks
par: Garnier, Paul, et autres
Publié: (2025)

Differentiable Turbulence: Closure as a partial differential equation constrained optimization
par: Shankar, Varun, et autres
Publié: (2023)

Revisit the simplified lattice Boltzmann method: dissipation, dispersion and stability
par: He, Zhengwei, et autres
Publié: (2026)

On the Preprocessing of Physics-informed Neural Networks: How to Better Utilize Data in Fluid Mechanics
par: Xu, Shengfeng, et autres
Publié: (2024)

A Framework of Data Assimilation for Wind Flow Fields by Physics-informed Neural Networks
par: Yan, Chang, et autres
Publié: (2024)

Learnable-Differentiable Finite Volume Solver for Accelerated Simulation of Flows
par: Yan, Mengtao, et autres
Publié: (2025)

Physics-enhanced Neural Operator for Simulating Turbulent Transport
par: Chen, Shengyu, et autres
Publié: (2024)

NeuralFluid: Neural Fluidic System Design and Control with Differentiable Simulation
par: Li, Yifei, et autres
Publié: (2024)

Parameter-Aware Ensemble SINDy for Interpretable Symbolic SGS Closure
par: Kang, Hanseul, et autres
Publié: (2025)

Enhancing Arterial Blood Flow Simulations through Physics-Informed Neural Networks
par: Bhargava, Shivam, et autres
Publié: (2024)

Learned Coarse Models for Efficient Turbulence Simulation
par: Stachenfeld, Kimberly, et autres
Publié: (2021)

Kinematic Closure of Drop Impact
par: Abbot, Mete, et autres
Publié: (2026)

Physics-Informed Neural Networks for Enhanced Interface Preservation in Lattice Boltzmann Multiphase Simulations
par: Li, Yue, et autres
Publié: (2025)

Physics-Based Machine Learning Closures and Wall Models for Hypersonic Transition-Continuum Boundary Layer Predictions
par: Nair, Ashish S., et autres
Publié: (2025)

Physics-guided surrogate learning enables zero-shot control of turbulent wings
par: Wang, Yuning, et autres
Publié: (2026)

Discovery of Sparse Invariant Subgrid-Scale Closures via Dissipation-Controlled Training for Large Eddy Simulation on Anisotropic Grids
par: Friess, Samantha, et autres
Publié: (2026)

Quantifying Out-of-Training Uncertainty of Neural-Network based Turbulence Closures
par: Grogan, Cody, et autres
Publié: (2025)

ELPINN: Eulerian Lagrangian Physics-Informed Neural Network
par: Thakur, Sukirt, et autres
Publié: (2025)

Subspace Acceleration for Efficient Nonlinear Water Wave Simulation
par: Sørensen, Rasmus Kleist Hørlyck, et autres
Publié: (2025)