Peng Chen
Orcid: 0000-0003-4215-9901Affiliations:
- University of Texas at Austin, Institute for Computational Engineering and Sciences, Austin, TX, USA
According to our database1,
Peng Chen
authored at least 20 papers
between 2016 and 2024.
Collaborative distances:
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Bibliography
2024
Derivative-Informed Neural Operator: An efficient framework for high-dimensional parametric derivative learning.
J. Comput. Phys., January, 2024
2023
Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network.
J. Sci. Comput., April, 2023
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design.
SIAM/ASA J. Uncertain. Quantification, March, 2023
An Offline-Online Decomposition Method for Efficient Linear Bayesian Goal-Oriented Optimal Experimental Design: Application to Optimal Sensor Placement.
SIAM J. Sci. Comput., February, 2023
Optimal design of chemoepitaxial guideposts for the directed self-assembly of block copolymer systems using an inexact Newton algorithm.
J. Comput. Phys., 2023
Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data.
CoRR, 2023
Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators.
CoRR, 2023
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
2022
Derivative-informed projected neural network for large-scale Bayesian optimal experimental design.
CoRR, 2022
2021
Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters.
SIAM/ASA J. Uncertain. Quantification, 2021
J. Comput. Phys., 2021
A fast and scalable computational framework for goal-oriented linear Bayesian optimal experimental design: Application to optimal sensor placement.
CoRR, 2021
2020
Tensor Train Construction From Tensor Actions, With Application to Compression of Large High Order Derivative Tensors.
SIAM J. Sci. Comput., 2020
Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs.
CoRR, 2020
A fast and scalable computational framework for large-scale and high-dimensional Bayesian optimal experimental design.
CoRR, 2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
2019
Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty.
J. Comput. Phys., 2019
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
2016
Sparse-grid, reduced-basis Bayesian inversion: Nonaffine-parametric nonlinear equations.
J. Comput. Phys., 2016