most citedST-PINN: A Self-Training Physics-Informed Neural Network for Partial Differential Equations

2 citations · 3 across the 3 of their papers we have counts for

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cs.LG2023★ 1 cited

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…

cs.LG2023★ 2 cited

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),…