Physics-informed machine-learning (PIML) enables the integration of domain knowledge
with machine learning (ML) algorithms, which results in higher data efficiency and more …

Recent breakthroughs in computing power have made it feasible to use machine learning
and deep learning to advance scientific computing in many fields, including fluid mechanics …

In this study, novel physics-informed neural network (PINN) methods for coupling
neighboring support points and their derivative terms which are obtained by automatic …

Despite the great promise of the physics-informed neural networks (PINNs) in solving
forward and inverse problems, several technical challenges are present as roadblocks for …

Partial differential equations (PDEs) play a fundamental role in modeling and simulating
problems across a wide range of disciplines. Recent advances in deep learning have shown …

Physics-informed neural networks (PINNs) have recently emerged as promising data-driven
PDE solvers showing encouraging results on various PDEs. However, there is a …

Traditional data-driven deep learning models often struggle with high training costs, error
accumulation, and poor generalizability in complex physical processes. Physics-informed …

The physics-informed neural network (PINN) is a general deep learning framework for
simulating flows with limited or no labeled data. In the current study, we develop a physics …

物理信息神经网络(physics-informed neural network, PINN) 是将模型方程编码到神经网络中,
使网络在逼近定解条件或观测数据的同时最小化方程残差, 实现偏微分方程求解 …

Advancements in computing power have recently made it possible to utilize machine
learning and deep learning to push scientific computing forward in a range of disciplines …