activity
20162026
most citedOptimal design of acoustic metamaterial cloaks under uncertainty

64 citations · 299 across the 24 of their papers we have counts for

collaborators

24 papers

math.NA2026

RBF-Generated Finite Difference Method Coupled with Quadratic Programming for Solving PDEs on Surfaces with Derivative Boundary Conditions

Peng Chen, Shixiao Willing Jiang, Rongji Li +1

Derivative boundary conditions introduce challenges for mesh-free discretizations of PDEs on surfaces, especially when the domain is represented by randomly sampled point clouds. T…

math.OC2023★ 9 cited

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…

math.NA2023

A Greedy Sensor Selection Algorithm for Hyperparameterized Linear Bayesian Inverse Problems

Nicole Aretz, Peng Chen, Denise Degen +1

We consider optimal sensor placement for a family of linear Bayesian inverse problems characterized by a deterministic hyper-parameter. The hyper-parameter describes distinct confi…

math.NA2022★ 8 cited

Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning

Thomas O'Leary-Roseberry, Peng Chen, Umberto Villa +1

We propose derivative-informed neural operators (DINOs), a general family of neural networks to approximate operators as infinite-dimensional mappings from input function spaces to…

cs.LG2022★ 1 cited

Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex Optimization

Yifei Wang, Peng Chen, Mert Pilanci +1

The computation of Wasserstein gradient direction is essential for posterior sampling problems and scientific computing. The approximation of the Wasserstein gradient with finite s…

math.NA2022

Large-scale Bayesian optimal experimental design with derivative-informed projected neural network

Keyi Wu, Thomas O'Leary-Roseberry, Peng Chen +1

We address the solution of large-scale Bayesian optimal experimental design (OED) problems governed by partial differential equations (PDEs) with infinite-dimensional parameter fie…