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20212026
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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math.NA2026

Structure-Preserving Dimension Reduction and Surrogates for Partially Linear Bayesian Inverse Problems

Ziheng Zhang, Dingcheng Luo, Thomas O'Leary-Roseberry +2

Many Bayesian inverse problems are characterized by two parameter vectors with distinct roles. A medium property, operator coefficient, or geometry block enters the forward model n…

math.OC2026

Shape Derivative-Informed Neural Operators with Application to Risk-Averse Shape Optimization

Xindi Gong, Dingcheng Luo, Thomas O'Leary-Roseberry +2

Shape optimization under uncertainty (OUU) is computationally intensive for classical PDE-based methods due to the high cost of repeated sampling-based risk evaluation across many…