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Journal Articles
- 2023
[j7]Pengzhan Jin
, Zhen Zhang, Ioannis G. Kevrekidis, George Em Karniadakis:
Learning Poisson Systems and Trajectories of Autonomous Systems via Poisson Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 34(11): 8271-8283 (2023)- 2022
- [j6]Aiqing Zhu, Pengzhan Jin, Yifa Tang:
Approximation capabilities of measure-preserving neural networks. Neural Networks 147: 72-80 (2022) - [j5]Pengzhan Jin, Shuai Meng, Lu Lu:
MIONet: Learning Multiple-Input Operators via Tensor Product. SIAM J. Sci. Comput. 44(6): 3490- (2022) - 2021
- [j4]Lu Lu, Pengzhan Jin, Guofei Pang, Zhongqiang Zhang, George Em Karniadakis:
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nat. Mach. Intell. 3(3): 218-229 (2021) - 2020
- [j3]Pengzhan Jin, Lu Lu, Yifa Tang, George Em Karniadakis:
Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. Neural Networks 130: 85-99 (2020) - [j2]Pengzhan Jin, Zhen Zhang, Aiqing Zhu, Yifa Tang, George Em Karniadakis:
SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems. Neural Networks 132: 166-179 (2020) - [j1]Pengzhan Jin, Yifa Tang, Aiqing Zhu:
Unit Triangular Factorization of the Matrix Symplectic Group. SIAM J. Matrix Anal. Appl. 41(4): 1630-1650 (2020)
Conference and Workshop Papers
- 2022
- [c1]Aiqing Zhu, Pengzhan Jin, Beibei Zhu, Yifa Tang:
On Numerical Integration in Neural Ordinary Differential Equations. ICML 2022: 27527-27547
Informal and Other Publications
- 2024
- [i14]Jun Hu, Pengzhan Jin:
A hybrid iterative method based on MIONet for PDEs: Theory and numerical examples. CoRR abs/2402.07156 (2024) - [i13]Shanshan Xiao, Pengzhan Jin, Yifa Tang:
Learning solution operators of PDEs defined on varying domains via MIONet. CoRR abs/2402.15097 (2024) - [i12]Pengzhan Jin:
Shallow ReLU neural networks and finite elements. CoRR abs/2403.05809 (2024) - 2023
- [i11]Jun Hu, Pengzhan Jin:
Experimental observation on a low-rank tensor model for eigenvalue problems. CoRR abs/2302.00538 (2023) - 2022
- [i10]Pengzhan Jin, Shuai Meng, Lu Lu:
MIONet: Learning multiple-input operators via tensor product. CoRR abs/2202.06137 (2022) - [i9]Aiqing Zhu, Pengzhan Jin, Beibei Zhu, Yifa Tang:
On Numerical Integration in Neural Ordinary Differential Equations. CoRR abs/2206.07335 (2022) - [i8]Yifan Wang, Pengzhan Jin, Hehu Xie:
Tensor Neural Network and Its Numerical Integration. CoRR abs/2207.02754 (2022) - 2021
- [i7]Aiqing Zhu, Pengzhan Jin, Yifa Tang:
Approximation capabilities of measure-preserving neural networks. CoRR abs/2106.10911 (2021) - 2020
- [i6]Pengzhan Jin, Aiqing Zhu, George Em Karniadakis, Yifa Tang:
Symplectic networks: Intrinsic structure-preserving networks for identifying Hamiltonian systems. CoRR abs/2001.03750 (2020) - [i5]Aiqing Zhu, Pengzhan Jin, Yifa Tang:
Deep Hamiltonian networks based on symplectic integrators. CoRR abs/2004.13830 (2020) - [i4]Aiqing Zhu, Pengzhan Jin, Yifa Tang:
Inverse modified differential equations for discovery of dynamics. CoRR abs/2009.01058 (2020) - [i3]Pengzhan Jin, Zhen Zhang, Ioannis G. Kevrekidis, George Em Karniadakis:
Learning Poisson systems and trajectories of autonomous systems via Poisson neural networks. CoRR abs/2012.03133 (2020) - 2019
- [i2]Pengzhan Jin, Lu Lu, Yifa Tang, George E. Karniadakis:
Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. CoRR abs/1905.11427 (2019) - [i1]Lu Lu, Pengzhan Jin, George Em Karniadakis:
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators. CoRR abs/1910.03193 (2019)
Coauthor Index
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