Recent advances on machine learning for computational fluid dynamics: A survey
This paper explores the recent advancements in enhancing Computational Fluid Dynamics
(CFD) tasks through Machine Learning (ML) techniques. We begin by introducing …
(CFD) tasks through Machine Learning (ML) techniques. We begin by introducing …
Care: Modeling interacting dynamics under temporal environmental variation
Modeling interacting dynamical systems, such as fluid dynamics and intermolecular
interactions, is a fundamental research problem for understanding and simulating complex …
interactions, is a fundamental research problem for understanding and simulating complex …
Swarm reinforcement learning for adaptive mesh refinement
Abstract The Finite Element Method, an important technique in engineering, is aided by
Adaptive Mesh Refinement (AMR), which dynamically refines mesh regions to allow for a …
Adaptive Mesh Refinement (AMR), which dynamically refines mesh regions to allow for a …
Physics informed token transformer for solving partial differential equations
Solving partial differential equations (PDEs) is the core of many fields of science and
engineering. While classical approaches are often prohibitively slow, machine learning …
engineering. While classical approaches are often prohibitively slow, machine learning …
Graph ode with factorized prototypes for modeling complicated interacting dynamics
This paper studies the problem of modeling interacting dynamical systems, which is critical
for understanding physical dynamics and biological processes. Recent research …
for understanding physical dynamics and biological processes. Recent research …
Equivariant neural simulators for stochastic spatiotemporal dynamics
Neural networks are emerging as a tool for scalable data-driven simulation of high-
dimensional dynamical systems, especially in settings where numerical methods are …
dimensional dynamical systems, especially in settings where numerical methods are …
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution
Deep learning-based surrogate models have demonstrated remarkable advantages over
classical solvers in terms of speed, often achieving speedups of 10 to 1000 times over …
classical solvers in terms of speed, often achieving speedups of 10 to 1000 times over …
The novel graph transformer-based surrogate model for learning physical systems
B Feng, XP Zhou - Computer Methods in Applied Mechanics and …, 2024 - Elsevier
Predicting physical systems over long-term horizons has a significant challenge. Although
prevalent machine learning techniques, such as Physics-Informed Neural Networks (PINNs) …
prevalent machine learning techniques, such as Physics-Informed Neural Networks (PINNs) …
[HTML][HTML] A reinforcement learning strategy for p-adaptation in high order solvers
Reinforcement learning (RL) has emerged as a promising approach to automating decision
processes. This paper explores the application of RL techniques to optimise the polynomial …
processes. This paper explores the application of RL techniques to optimise the polynomial …
Predicting dynamic responses of continuous deformable bodies: A graph-based learning approach
Predicting dynamic responses of continuous deformable bodies (CDBs) is essential for
various research fields, including civil engineering and computer graphics. Machine …
various research fields, including civil engineering and computer graphics. Machine …