Peng Chen - RankMe

31 papers A* 3Journal 28

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Tucker Tensor Train Taylor Series. CoRR Nick Alger, Blake Christierson, Peng Chen, Omar Ghattas
2025	J	`jnl`	Dimension reduction for derivative-informed operator learning: An analysis of approximation errors. CoRR Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas
2025	J	`jnl`	Efficient PDE-Constrained Optimization Under High-Dimensional Uncertainty Using Derivative-Informed Neural Operators. SIAM J. Sci. Comput. Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas
2025	J	`jnl`	HunyuanImage 3.0 Technical Report. CoRR Siyu Cao, Hangting Chen, Peng Chen, Yiji Cheng, Yutao Cui, Xinchi Deng, Ying Dong, Kipper Gong, Tianpeng Gu, Xiusen Gu, Tiankai Hang, Duojun Huang, Jie Jiang, Zhengkai Jiang, Weijie Kong, Changlin Li, Donghao Li, Junzhe Li, Xin Li, Yang Li, Zhenxi Li, Zhimin Li, Jiaxin Lin, Linus, Lucaz Liu, Shu Liu, Songtao Liu, Yu Liu, Yuhong Liu, Yanxin Long, Fanbin Lu, Qinglin Lu, Yuyang Peng, Yuanbo Peng, Xiangwei Shen, Yixuan Shi, Jiale Tao, Yangyu Tao, Qi Tian, Pengfei Wan, Chunyu Wang, Kai Wang, Lei Wang, Linqing Wang, Lucas Wang, Qixun Wang, Weiyan Wang, Hao Wen, Bing Wu, Jianbing Wu, Yue Wu, Senhao Xie, Fang Yang, Miles Yang, Xiaofeng Yang, Xuan Yang, Zhantao Yang, Jingmiao Yu, Zheng Yuan, Chao Zhang, Jian-Wei Zhang, Peizhen Zhang, Shi-Xue Zhang, Tao Zhang, Weigang Zhang, Yepeng Zhang, Yingfang Zhang, Zihao Zhang, Zijian Zhang, Penghao Zhao, Zhiyuan Zhao, Xuefei Zhe, Jianchen Zhu, Zhao Zhong
2024	J	`jnl`	Derivative-Informed Neural Operator: An efficient framework for high-dimensional parametric derivative learning. J. Comput. Phys. Thomas O'Leary-Roseberry, Peng Chen, Umberto Villa, Omar Ghattas
2024	J	`jnl`	Gaussian mixture Taylor approximations of risk measures constrained by PDEs with Gaussian random field inputs. CoRR Dingcheng Luo, Joshua Chen, Peng Chen, Omar Ghattas
2024	J	`jnl`	SOUPy: Stochastic PDE-constrained optimization under high-dimensional uncertainty in Python. J. Open Source Softw. Dingcheng Luo, Peng Chen, Thomas O'Leary-Roseberry, Umberto Villa, Omar Ghattas
2023	J	`jnl`	A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design. SIAM/ASA J. Uncertain. Quantification Keyi Wu, Peng Chen, Omar Ghattas
2023	J	`jnl`	An Offline-Online Decomposition Method for Efficient Linear Bayesian Goal-Oriented Optimal Experimental Design: Application to Optimal Sensor Placement. SIAM J. Sci. Comput. Keyi Wu, Peng Chen, Omar Ghattas
2023	J	`jnl`	Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data. CoRR Lianghao Cao, Keyi Wu, J. Tinsley Oden, Peng Chen, Omar Ghattas
2023	J	`jnl`	Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators. CoRR Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas
2023	J	`jnl`	Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network. J. Sci. Comput. Keyi Wu, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas
2023	J	`jnl`	Optimal design of chemoepitaxial guideposts for the directed self-assembly of block copolymer systems using an inexact Newton algorithm. J. Comput. Phys. Dingcheng Luo, Lianghao Cao, Peng Chen, Omar Ghattas, J. Tinsley Oden
2023	A*	`conf`	Uncertainty Quantification in Deep Learning. KDD Lingkai Kong, Harshavardhan Kamarthi, Peng Chen, B. Aditya Prakash, Chao Zhang
2022	J	`jnl`	Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning. CoRR Thomas O'Leary-Roseberry, Peng Chen, Umberto Villa, Omar Ghattas
2022	J	`jnl`	Derivative-informed projected neural network for large-scale Bayesian optimal experimental design. CoRR Keyi Wu, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas
2022	J	`jnl`	Optimal design of chemoepitaxial guideposts for directed self-assembly of block copolymer systems using an inexact-Newton algorithm. CoRR Dingcheng Luo, Lianghao Cao, Peng Chen, Omar Ghattas, J. Tinsley Oden
2021	J	`jnl`	A fast and scalable computational framework for goal-oriented linear Bayesian optimal experimental design: Application to optimal sensor placement. CoRR Keyi Wu, Peng Chen, Omar Ghattas
2021	J	`jnl`	Optimal design of acoustic metamaterial cloaks under uncertainty. J. Comput. Phys. Peng Chen, Michael R. Haberman, Omar Ghattas
2021	J	`jnl`	Stein Variational Reduced Basis Bayesian Inversion. SIAM J. Sci. Comput. Peng Chen, Omar Ghattas
2021	J	`jnl`	Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters. SIAM/ASA J. Uncertain. Quantification Peng Chen, Omar Ghattas
2020	J	`jnl`	A fast and scalable computational framework for large-scale and high-dimensional Bayesian optimal experimental design. CoRR Keyi Wu, Peng Chen, Omar Ghattas
2020	J	`jnl`	Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs. CoRR Thomas O'Leary-Roseberry, Umberto Villa, Peng Chen, Omar Ghattas
2020	A*	`conf`	Projected Stein Variational Gradient Descent. NeurIPS Peng Chen, Omar Ghattas
2020	J	`jnl`	Projected Stein Variational Gradient Descent. CoRR Peng Chen, Omar Ghattas
2020	J	`jnl`	Stein variational reduced basis Bayesian inversion. CoRR Peng Chen, Omar Ghattas
2020	J	`jnl`	Tensor Train Construction From Tensor Actions, With Application to Compression of Large High Order Derivative Tensors. SIAM J. Sci. Comput. Nick Alger, Peng Chen, Omar Ghattas
2020	J	`jnl`	Tensor train construction from tensor actions, with application to compression of large high order derivative tensors. CoRR Nick Alger, Peng Chen, Omar Ghattas
2019	A*	`conf`	Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions. NeurIPS Peng Chen, Keyi Wu, Joshua Chen, Tom O'Leary-Roseberry, Omar Ghattas
2019	J	`jnl`	Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty. J. Comput. Phys. Peng Chen, Umberto Villa, Omar Ghattas
2016	J	`jnl`	Sparse-grid, reduced-basis Bayesian inversion: Nonaffine-parametric nonlinear equations. J. Comput. Phys. Peng Chen, Christoph Schwab