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20222024
most citedVI-PINNs: Variance-involved Physics-informed Neural Networks for Fast and Accurate Prediction of Partial Differential Equations

3 citations · 7 across the 5 of their papers we have counts for

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Showing 2023Show all

2 papers · 1 filter

cs.NE2023★ 1 cited

Efficient Discrete Physics-informed Neural Networks for Addressing Evolutionary Partial Differential Equations

Siqi Chen, Bin Shan, Ye Li

Physics-informed neural networks (PINNs) have shown promising potential for solving partial differential equations (PDEs) using deep learning. However, PINNs face training difficul…

cs.LG2023★ 1 cited

Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks

Ye Li, Song-Can Chen, Sheng-Jun Huang

Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems, but they are still trapped in training fa…