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ORCIDPeng Chen 0024
Person information
- affiliation: University of Texas at Austin, Institute for Computational Engineering and Sciences, Austin, TX, USA
Other persons with the same name
- Peng Chen — disambiguation page
- Peng Chen 0001
![[0000-0002-5810-8159]](https://dblp1.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-5810-8159")
— Anhui University, Institute of Health Sciences, Hefei, China (and 2 more) - Peng Chen 0002 — Mie University, Faculty of Bioresources, Tsu, Japan (and 1 more)
- Peng Chen 0003 — Central South University, Changsha
- Peng Chen 0004 — University of Science and Technology of China
- Peng Chen 0005 — East China Normal University
- Peng Chen 0006 — Florida State University (and 1 more)
- Peng Chen 0007 — Xihua University, School of Computer and Software Engineering, Chengdu, China (and 1 more)
- Peng Chen 0008 — Zhejiang University, College of Information and Engineering, Hangzhou, China
- Peng Chen 0009 — Beijing University of Posts and Telecommunications
- Peng Chen 0010 — Kyushu Institute of Technology
- Peng Chen 0011 — China Three Gorges University
- Peng Chen 0012 — University of North Texas
- Peng Chen 0013 — North China University of Water Conservancy and Hydroelectric Power
- Peng Chen 0014 — Peking University, Shenzhen, China
- Peng Chen 0015 — Indiana University, Bloomington, USA
- Peng Chen 0016 — ETH Zurich, Switzerland
- Peng Chen 0017 — University of Electronic Science and Technology of China, School of Aeronautics and Astronautics, Chengdu, China
- Peng Chen 0018 — Southeast University, School of Information Science and Engineering, Nanjing, China (and 1 more)
- Peng Chen 0019 — State Oceanic Administration, Hangzhou, China
- Peng Chen 0020 — Northwestern Polytechnical University, School of Marine Science and Technology, Xi'an, China
- Peng Chen 0021 — Beihang University, School of Transportation Science and Engineering, Beijing, China
- Peng Chen 0022 — University College Dublin, School of Electrical and Electronic Engineering, Ireland (and 1 more)
- Peng Chen 0023 — Second Institute of Oceanography, State Key Laboratory of Satellite Ocean Environment Dynamics, Hangzhou, China (and 1 more)
- Peng Chen 0025 — Tongji University, College of Surveying and Geo-Informatics, Shanghai, China
- Peng Chen 0026 — Dalian Maritime University, Navigation College, Dalian, China (and 1 more)
- Peng Chen 0027 — National University of Singapore, Singapore (and 1 more)
- Peng Chen 0028 — China Telecom, Technology Innovation Center, Beijing, China
- Peng Chen 0029 — Microsoft Research and AI Group, Beijing, China
- Peng Chen 0030 — Northeastern University, College of Information Science and Engineering, Shenyang, China
- Peng Chen 0031 — University of Science and Technology Beijing, School of Automation and Electrical Engineering, China
- Peng Chen 0032 — Shanghai Maritime University, Department of Computer Science and Technology, China
- Peng Chen 0033 — Jilin University, Second Hospital, Changchun, China
- Peng Chen 0034 — ByteDance AI Lab, Beijing, China (and 1 more)
- Peng Chen 0035 — National Institute of Advanced Industrial Science and Technology, Japan, RIKEN Center for Computational Science, Tokyo, Japan (and 1 more)
- Peng Chen 0036 — Dalian Maritime University of China, Navigation College, Key Laboratory of Transport Industry of Intelligent Water Transportation, China
- Peng Chen 0037 — University of Adelaide, School of Computer Science, SA, Australia (and 2 more)
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Journal Articles
- 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) - 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) - 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) - 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) - 2016
- [j1]Peng Chen, Christoph Schwab:
Sparse-grid, reduced-basis Bayesian inversion: Nonaffine-parametric nonlinear equations. J. Comput. Phys. 316: 470-503 (2016)
Conference and Workshop Papers
- 2023
- [c3]Lingkai Kong, Harshavardhan Kamarthi, Peng Chen, B. Aditya Prakash, Chao Zhang:
Uncertainty Quantification in Deep Learning. KDD 2023: 5809-5810 - 2020
- [c2]Peng Chen, Omar Ghattas:
Projected Stein Variational Gradient Descent. NeurIPS 2020 - 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
Informal and Other Publications
- 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
- [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
- [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)
Coauthor Index
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last updated on 2024-07-24 00:25 CEST by the dblp team
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