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Peng Chen
ORCID
Publication Activity (10 Years)
Years Active: 2019-2024
Publications (10 Years): 24
Top Topics
High Dimensions
Computational Framework
Experimental Design
Bayesian Inference
Top Venues
CoRR
J. Comput. Phys.
SIAM J. Sci. Comput.
SIAM/ASA J. Uncertain. Quantification
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Publications
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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)