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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…
math.OC2025
Taylor Approximation Variance Reduction for Approximation Errors in PDE-constrained Bayesian Inverse Problems
Ruanui Nicholson, Radoslav Vuchkov, Umberto Villa +1
In numerous applications, surrogate models are used as a replacement for accurate parameter-to-observable mappings when solving large-scale inverse problems governed by partial dif…