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

3 citations · 3 across the 1 of their papers we have counts for

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cs.LG2024

Improving Generalization of Deep Neural Networks by Optimum Shifting

Yuyan Zhou, Ye Li, Lei Feng +1

Recent studies showed that the generalization of neural networks is correlated with the sharpness of the loss landscape, and flat minima suggests a better generalization ability th…

cs.LG2022★ 3 cited

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

Bin Shan, Ye Li, Shengjun Huang

Although physics-informed neural networks(PINNs) have progressed a lot in many real applications recently, there remains problems to be further studied, such as achieving more accu…