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most citedEfficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators

9 citations · 21 across the 8 of their papers we have counts for

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cs.LG2025

Derivative-Informed Fourier Neural Operator: Universal Approximation and Applications to PDE-Constrained Optimization

Boyuan Yao, Dingcheng Luo, Lianghao Cao +3

We present approximation theories and efficient training methods for derivative-informed Fourier neural operators (DIFNOs) with applications to PDE-constrained optimization. A DIFN…

math.NA2025

Dimension reduction for derivative-informed operator learning: An analysis of approximation errors

Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen +1

We study the derivative-informed learning of nonlinear operators between infinite-dimensional separable Hilbert spaces by neural networks. Such operators can arise from the solutio…