9 citations · 15 across the 4 of their papers we have counts for
4 papers
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…
Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators
Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen +1
We propose a novel machine learning framework for solving optimization problems governed by large-scale partial differential equations (PDEs) with high-dimensional random parameter…
Investigating Steady Unconfined Groundwater Flow using Physics Informed Neural Networks
Mohammad Afzal Shadab, DingCheng Luo, Yiran Shen +2
A novel deep learning technique called Physics Informed Neural Networks (PINNs) is adapted to study steady groundwater flow in unconfined aquifers. This technique utilizes informat…