2 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
LLM4Fluid: Large Language Models as Generalizable Neural Solvers for Fluid Dynamics
Qisong Xiao, Xinhai Chen, Qinglin Wang +10
Deep learning has emerged as a promising paradigm for spatio-temporal modeling of fluid dynamics. However, existing approaches often suffer from limited generalization to unseen fl…
GNNRL-Smoothing: A Prior-Free Reinforcement Learning Model for Mesh Smoothing
Zhichao Wang, Xinhai Chen, Chunye Gong +5
Mesh smoothing methods can enhance mesh quality by eliminating distorted elements, leading to improved convergence in simulations. To balance the efficiency and robustness of tradi…
Auxiliary-Tasks Learning for Physics-Informed Neural Network-Based Partial Differential Equations Solving
Junjun Yan, Xinhai Chen, Zhichao Wang +2
Physics-informed neural networks (PINNs) have emerged as promising surrogate modes for solving partial differential equations (PDEs). Their effectiveness lies in the ability to cap…
ST-PINN: A Self-Training Physics-Informed Neural Network for Partial Differential Equations
Junjun Yan, Xinhai Chen, Zhichao Wang +2
Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning, physics-informed neural networks (PINNs),…