DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators. - Lu Lu 010; Pengzhan Jin; George Em Karniadakis (CoRR, 2019)

Symplectic networks: Intrinsic structure-preserving networks for identifying Hamiltonian systems. - Pengzhan Jin; Aiqing Zhu; George Em Karniadakis; Yifa Tang (CoRR, 2020)

Deep Hamiltonian networks based on symplectic integrators. - Aiqing Zhu; Pengzhan Jin; Yifa Tang (CoRR, 2020)

Inverse modified differential equations for discovery of dynamics. - Aiqing Zhu; Pengzhan Jin; Yifa Tang (CoRR, 2020)

Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. - Pengzhan Jin; Lu Lu 010; Yifa Tang; George Em Karniadakis (Neural Networks, 2020)

SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems. - Pengzhan Jin; Zhen Zhang 029; Aiqing Zhu; Yifa Tang; George Em Karniadakis (Neural Networks, 2020)

Unit Triangular Factorization of the Matrix Symplectic Group. - Pengzhan Jin; Yifa Tang; Aiqing Zhu (SIAM J. Matrix Anal. Appl., 2020)

Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. - Lu Lu 010; Pengzhan Jin; Guofei Pang; Zhongqiang Zhang; George Em Karniadakis (Nat. Mach. Intell., 2021)

On Numerical Integration in Neural Ordinary Differential Equations. - Aiqing Zhu; Pengzhan Jin; Beibei Zhu; Yifa Tang (ICML, 2022)

Tensor Neural Network and Its Numerical Integration. - Yifan Wang; Pengzhan Jin; Hehu Xie (CoRR, 2022)

Approximation capabilities of measure-preserving neural networks. - Aiqing Zhu; Pengzhan Jin; Yifa Tang (Neural Networks, 2022)

MIONet: Learning Multiple-Input Operators via Tensor Product. - Pengzhan Jin; Shuai Meng; Lu Lu 010 (SIAM J. Sci. Comput., 2022)

Experimental observation on a low-rank tensor model for eigenvalue problems. - Jun Hu; Pengzhan Jin (CoRR, 2023)

Learning Poisson Systems and Trajectories of Autonomous Systems via Poisson Neural Networks. - Pengzhan Jin; Zhen Zhang 029; Ioannis G. Kevrekidis; George Em Karniadakis (IEEE Trans. Neural Networks Learn. Syst., 2023)

A hybrid iterative method based on MIONet for PDEs: Theory and numerical examples. - Jun Hu; Pengzhan Jin (CoRR, 2024)

Learning solution operators of PDEs defined on varying domains via MIONet. - Shanshan Xiao; Pengzhan Jin; Yifa Tang (CoRR, 2024)

Shallow ReLU neural networks and finite elements. - Pengzhan Jin (CoRR, 2024)

A deformation-based framework for learning solution mappings of PDEs defined on varying domains. - Shanshan Xiao; Pengzhan Jin; Yifa Tang (CoRR, 2024)