Peng Chen

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 24

Top Topics

High Dimensions

Computational Framework

Experimental Design

Bayesian Inference

Top Venues

J. Comput. Phys.

SIAM J. Sci. Comput.

SIAM/ASA J. Uncertain. Quantification

Publications

Thomas O'Leary-Roseberry, Peng Chen, Umberto Villa, Omar Ghattas
Derivative-Informed Neural Operator: An efficient framework for high-dimensional parametric derivative learning. J. Comput. Phys. 496 (2024)
Keyi Wu, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas
Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network. J. Sci. Comput. 95 (1) (2023)
Keyi Wu, Peng Chen, Omar Ghattas
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design. SIAM/ASA J. Uncertain. Quantification 11 (1) (2023)
Lianghao Cao, Keyi Wu, J. Tinsley Oden, Peng Chen, Omar Ghattas
Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data. CoRR (2023)
Keyi Wu, Peng Chen, Omar Ghattas
An Offline-Online Decomposition Method for Efficient Linear Bayesian Goal-Oriented Optimal Experimental Design: Application to Optimal Sensor Placement. SIAM J. Sci. Comput. 45 (1) (2023)
Dingcheng Luo, Lianghao Cao, Peng Chen, Omar Ghattas, J. Tinsley Oden
Optimal design of chemoepitaxial guideposts for the directed self-assembly of block copolymer systems using an inexact Newton algorithm. J. Comput. Phys. 485 (2023)
Lingkai Kong, Harshavardhan Kamarthi, Peng Chen, B. Aditya Prakash, Chao Zhang
Uncertainty Quantification in Deep Learning. KDD (2023)
Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas
Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators. CoRR (2023)
Dingcheng Luo, Lianghao Cao, Peng Chen, Omar Ghattas, J. Tinsley Oden
Optimal design of chemoepitaxial guideposts for directed self-assembly of block copolymer systems using an inexact-Newton algorithm. CoRR (2022)
Thomas O'Leary-Roseberry, Peng Chen, Umberto Villa, Omar Ghattas
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning. CoRR (2022)
Keyi Wu, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas
Derivative-informed projected neural network for large-scale Bayesian optimal experimental design. CoRR (2022)
Keyi Wu, Peng Chen, Omar Ghattas
A fast and scalable computational framework for goal-oriented linear Bayesian optimal experimental design: Application to optimal sensor placement. CoRR (2021)
Peng Chen, Omar Ghattas
Stein Variational Reduced Basis Bayesian Inversion. SIAM J. Sci. Comput. 43 (2) (2021)
Peng Chen, Omar Ghattas
Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters. SIAM/ASA J. Uncertain. Quantification 9 (4) (2021)
Peng Chen, Michael R. Haberman, Omar Ghattas
Optimal design of acoustic metamaterial cloaks under uncertainty. J. Comput. Phys. 431 (2021)
Nick Alger, Peng Chen, Omar Ghattas
Tensor train construction from tensor actions, with application to compression of large high order derivative tensors. CoRR (2020)
Peng Chen, Omar Ghattas
Projected Stein Variational Gradient Descent. CoRR (2020)
Peng Chen, Omar Ghattas
Stein variational reduced basis Bayesian inversion. CoRR (2020)
Thomas O'Leary-Roseberry, Umberto Villa, Peng Chen, Omar Ghattas
Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs. CoRR (2020)
Nick Alger, Peng Chen, Omar Ghattas
Tensor Train Construction From Tensor Actions, With Application to Compression of Large High Order Derivative Tensors. SIAM J. Sci. Comput. 42 (5) (2020)
Keyi Wu, Peng Chen, Omar Ghattas
A fast and scalable computational framework for large-scale and high-dimensional Bayesian optimal experimental design. CoRR (2020)
Peng Chen, Omar Ghattas
Projected Stein Variational Gradient Descent. NeurIPS (2020)
Peng Chen, Umberto Villa, Omar Ghattas
Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty. J. Comput. Phys. 385 (2019)
Peng Chen, Keyi Wu, Joshua Chen, Tom O'Leary-Roseberry, Omar Ghattas
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions. NeurIPS (2019)