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20232026
most citedAutomatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations

1 citations · 2 across the 9 of their papers we have counts for

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math.NA2025

Prescribed-Basis Coefficient-to-Coefficient Neural Operator for Partial Differential Equations

Chuqi Chen, Yang Xiang, Weihong Zhang

Operator learning provides a data-driven approach to approximating solution operators of partial differential equations, but its effectiveness depends strongly on how input and out…

math.NA2025

SSBE-PINN: A Sobolev Boundary Scheme Boosting Stability and Accuracy in Elliptic/Parabolic PDE Learning

Qixuan Zhou, Chuqi Chen, Tao Luo +1

Physics-Informed Neural Networks (PINNs) have emerged as a powerful framework for solving partial differential equations (PDEs), yet they often fail to achieve accurate convergence…