Shanda Li

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20212023

most citedIs

L^{2}

Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network?

25 citations · 63 across the 6 of their papers we have counts for

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Showing 2022Show all

3 papers · 1 filter

cs.LG2022★ 25 cited

Is $L^{2}$ Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network?

Chuwei Wang, Shanda Li, Di He +1

The Physics-Informed Neural Network (PINN) approach is a new and promising way to solve partial differential equations using deep learning. The $L^{2}$ Physics-Informed Loss is the d…

cs.LG2022★ 12 cited

Your Transformer May Not be as Powerful as You Expect

Shengjie Luo, Shanda Li, Shuxin Zheng +3

Relative Positional Encoding (RPE), which encodes the relative distance between any pair of tokens, is one of the most successful modifications to the original Transformer. As far…

cs.LG2022★ 9 cited

Learning Physics-Informed Neural Networks without Stacked Back-propagation

Di He, Shanda Li, Wenlei Shi +5

Physics-Informed Neural Network (PINN) has become a commonly used machine learning approach to solve partial differential equations (PDE). But, facing high-dimensional secondorder…

Is L2 Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network?

Your Transformer May Not be as Powerful as You Expect

Learning Physics-Informed Neural Networks without Stacked Back-propagation

Is $L^{2}$ Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network?