9 citations · 21 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…
Gaussian mixture Taylor approximations of risk measures constrained by PDEs with Gaussian random field inputs
Dingcheng Luo, Joshua Chen, Peng Chen +1
This work considers the computation of risk measures for quantities of interest governed by PDEs with Gaussian random field parameters using Taylor approximations. While efficient,…
Inference of Heterogeneous Material Properties via Infinite-Dimensional Integrated DIC
Joseph Kirchhoff, Dingcheng Luo, Thomas O'Leary-Roseberry +1
We present a scalable and efficient framework for the inference of spatially-varying parameters of continuum materials from image observations of their deformations. Our goal is th…