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Peng Chen 0024
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Publications
- 2024
- [j11]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: 112555 (2024) - 2023
- [j10]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: 112101 (2023) - [j9]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): 30 (2023) - [j8]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): 235-261 (2023) - [j7]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): 57- (2023) - [i11]Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas:
Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators. CoRR abs/2305.20053 (2023) - [i10]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 abs/2306.05398 (2023) - 2022
- [i9]Keyi Wu, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas:
Derivative-informed projected neural network for large-scale Bayesian optimal experimental design. CoRR abs/2201.07925 (2022) - [i8]Thomas O'Leary-Roseberry, Peng Chen, Umberto Villa, Omar Ghattas:
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning. CoRR abs/2206.10745 (2022) - [i7]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 abs/2208.01193 (2022) - 2021
- [j6]Peng Chen, Michael R. Haberman, Omar Ghattas:
Optimal design of acoustic metamaterial cloaks under uncertainty. J. Comput. Phys. 431: 110114 (2021) - [j5]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): 1381-1410 (2021) - [j4]Peng Chen, Omar Ghattas:
Stein Variational Reduced Basis Bayesian Inversion. SIAM J. Sci. Comput. 43(2): A1163-A1193 (2021) - [i6]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 abs/2102.06627 (2021) - 2020
- [j3]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): A3516-A3539 (2020) - [c2]Peng Chen, Omar Ghattas:
Projected Stein Variational Gradient Descent. NeurIPS 2020 - [i5]Peng Chen, Omar Ghattas:
Projected Stein Variational Gradient Descent. CoRR abs/2002.03469 (2020) - [i4]Nick Alger, Peng Chen, Omar Ghattas:
Tensor train construction from tensor actions, with application to compression of large high order derivative tensors. CoRR abs/2002.06244 (2020) - [i3]Peng Chen, Omar Ghattas:
Stein variational reduced basis Bayesian inversion. CoRR abs/2002.10924 (2020) - [i2]Keyi Wu, Peng Chen, Omar Ghattas:
A fast and scalable computational framework for large-scale and high-dimensional Bayesian optimal experimental design. CoRR abs/2010.15196 (2020) - [i1]Thomas O'Leary-Roseberry, Umberto Villa, Peng Chen, Omar Ghattas:
Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs. CoRR abs/2011.15110 (2020) - 2019
- [j2]Peng Chen, Umberto Villa, Omar Ghattas:
Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty. J. Comput. Phys. 385: 163-186 (2019) - [c1]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: 15104-15113
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