Sparse-grid, reduced-basis Bayesian inversion: Nonaffine-parametric nonlinear equations. - Peng Chen 024; Christoph Schwab (J. Comput. Phys., 2016)

Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions. - Peng Chen 024; Keyi Wu; Joshua Chen; Tom O'Leary-Roseberry; Omar Ghattas (NeurIPS, 2019)

Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty. - Peng Chen 024; Umberto Villa; Omar Ghattas (J. Comput. Phys., 2019)

Projected Stein Variational Gradient Descent. - Peng Chen 024; Omar Ghattas (NeurIPS, 2020)

A fast and scalable computational framework for large-scale and high-dimensional Bayesian optimal experimental design. - Keyi Wu; Peng Chen 024; Omar Ghattas (CoRR, 2020)

Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs. - Thomas O'Leary-Roseberry; Umberto Villa; Peng Chen 024; Omar Ghattas (CoRR, 2020)

Tensor Train Construction From Tensor Actions, With Application to Compression of Large High Order Derivative Tensors. - Nick Alger; Peng Chen 024; Omar Ghattas (SIAM J. Sci. Comput., 2020)

A fast and scalable computational framework for goal-oriented linear Bayesian optimal experimental design: Application to optimal sensor placement. - Keyi Wu; Peng Chen 024; Omar Ghattas (CoRR, 2021)

Optimal design of acoustic metamaterial cloaks under uncertainty. - Peng Chen 024; Michael R. Haberman; Omar Ghattas (J. Comput. Phys., 2021)

Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters. - Peng Chen 024; Omar Ghattas (SIAM/ASA J. Uncertain. Quantification, 2021)

Stein Variational Reduced Basis Bayesian Inversion. - Peng Chen 024; Omar Ghattas (SIAM J. Sci. Comput., 2021)

Derivative-informed projected neural network for large-scale Bayesian optimal experimental design. - Keyi Wu; Thomas O'Leary-Roseberry; Peng Chen 024; Omar Ghattas (CoRR, 2022)

Uncertainty Quantification in Deep Learning. - Lingkai Kong; Harshavardhan Kamarthi; Peng Chen 024; B. Aditya Prakash; Chao Zhang 014 (KDD, 2023)

Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators. - Dingcheng Luo; Thomas O'Leary-Roseberry; Peng Chen 024; Omar Ghattas (CoRR, 2023)

Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data. - Lianghao Cao; Keyi Wu; J. Tinsley Oden; Peng Chen 024; Omar Ghattas (CoRR, 2023)

Optimal design of chemoepitaxial guideposts for the directed self-assembly of block copolymer systems using an inexact Newton algorithm. - Dingcheng Luo; Lianghao Cao; Peng Chen 024; Omar Ghattas; J. Tinsley Oden (J. Comput. Phys., 2023)

An Offline-Online Decomposition Method for Efficient Linear Bayesian Goal-Oriented Optimal Experimental Design: Application to Optimal Sensor Placement. - Keyi Wu; Peng Chen 024; Omar Ghattas (SIAM J. Sci. Comput., 2023)

A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design. - Keyi Wu; Peng Chen 024; Omar Ghattas (SIAM/ASA J. Uncertain. Quantification, 2023)

Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network. - Keyi Wu; Thomas O'Leary-Roseberry; Peng Chen 024; Omar Ghattas (J. Sci. Comput., 2023)

Gaussian mixture Taylor approximations of risk measures constrained by PDEs with Gaussian random field inputs. - Dingcheng Luo; Joshua Chen; Peng Chen 024; Omar Ghattas (CoRR, 2024)

SOUPy: Stochastic PDE-constrained optimization under high-dimensional uncertainty in Python. - Dingcheng Luo; Peng Chen 024; Thomas O'Leary-Roseberry; Umberto Villa; Omar Ghattas (J. Open Source Softw., 2024)

Derivative-Informed Neural Operator: An efficient framework for high-dimensional parametric derivative learning. - Thomas O'Leary-Roseberry; Peng Chen 024; Umberto Villa; Omar Ghattas (J. Comput. Phys., 2024)

Dimension reduction for derivative-informed operator learning: An analysis of approximation errors. - Dingcheng Luo; Thomas O'Leary-Roseberry; Peng Chen 024; Omar Ghattas (CoRR, 2025)