Pengzhan Jin - RankMe

26 papers A* 1Journal 25

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Two-hidden-layer ReLU neural networks and finite elements. Neural Networks Pengzhan Jin
2025	J	`jnl`	A hybrid iterative method based on MIONet for PDEs: Theory and numerical examples. Math. Comput. Jun Hu, Pengzhan Jin
2025	J	`jnl`	Manifold Function Encoder: Identifying Different Functions Defined on Different Manifolds. CoRR Jun Hu, Pengzhan Jin, Weijun Zhang
2024	J	`jnl`	A deformation-based framework for learning solution mappings of PDEs defined on varying domains. CoRR Shanshan Xiao, Pengzhan Jin, Yifa Tang
2024	J	`jnl`	A hybrid iterative method based on MIONet for PDEs: Theory and numerical examples. CoRR Jun Hu, Pengzhan Jin
2024	J	`jnl`	Learning solution operators of PDEs defined on varying domains via MIONet. CoRR Shanshan Xiao, Pengzhan Jin, Yifa Tang
2024	J	`jnl`	Shallow ReLU neural networks and finite elements. CoRR Pengzhan Jin
2023	J	`jnl`	Experimental observation on a low-rank tensor model for eigenvalue problems. CoRR Jun Hu, Pengzhan Jin
2023	J	`jnl`	Learning Poisson Systems and Trajectories of Autonomous Systems via Poisson Neural Networks. IEEE Trans. Neural Networks Learn. Syst. Pengzhan Jin, Zhen Zhang, Ioannis G. Kevrekidis, George Em Karniadakis
2022	J	`jnl`	Approximation capabilities of measure-preserving neural networks. Neural Networks Aiqing Zhu, Pengzhan Jin, Yifa Tang
2022	J	`jnl`	MIONet: Learning Multiple-Input Operators via Tensor Product. SIAM J. Sci. Comput. Pengzhan Jin, Shuai Meng, Lu Lu
2022	J	`jnl`	MIONet: Learning multiple-input operators via tensor product. CoRR Pengzhan Jin, Shuai Meng, Lu Lu
2022	A*	`conf`	On Numerical Integration in Neural Ordinary Differential Equations. ICML Aiqing Zhu, Pengzhan Jin, Beibei Zhu, Yifa Tang
2022	J	`jnl`	On Numerical Integration in Neural Ordinary Differential Equations. CoRR Aiqing Zhu, Pengzhan Jin, Beibei Zhu, Yifa Tang
2022	J	`jnl`	Tensor Neural Network and Its Numerical Integration. CoRR Yifan Wang, Pengzhan Jin, Hehu Xie
2021	J	`jnl`	Approximation capabilities of measure-preserving neural networks. CoRR Aiqing Zhu, Pengzhan Jin, Yifa Tang
2021	J	`jnl`	Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nat. Mach. Intell. Lu Lu, Pengzhan Jin, Guofei Pang, Zhongqiang Zhang, George Em Karniadakis
2020	J	`jnl`	Deep Hamiltonian networks based on symplectic integrators. CoRR Aiqing Zhu, Pengzhan Jin, Yifa Tang
2020	J	`jnl`	Inverse modified differential equations for discovery of dynamics. CoRR Aiqing Zhu, Pengzhan Jin, Yifa Tang
2020	J	`jnl`	Learning Poisson systems and trajectories of autonomous systems via Poisson neural networks. CoRR Pengzhan Jin, Zhen Zhang, Ioannis G. Kevrekidis, George Em Karniadakis
2020	J	`jnl`	Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. Neural Networks Pengzhan Jin, Lu Lu, Yifa Tang, George Em Karniadakis
2020	J	`jnl`	SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems. Neural Networks Pengzhan Jin, Zhen Zhang, Aiqing Zhu, Yifa Tang, George Em Karniadakis
2020	J	`jnl`	Symplectic networks: Intrinsic structure-preserving networks for identifying Hamiltonian systems. CoRR Pengzhan Jin, Aiqing Zhu, George Em Karniadakis, Yifa Tang
2020	J	`jnl`	Unit Triangular Factorization of the Matrix Symplectic Group. SIAM J. Matrix Anal. Appl. Pengzhan Jin, Yifa Tang, Aiqing Zhu
2019	J	`jnl`	DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators. CoRR Lu Lu, Pengzhan Jin, George Em Karniadakis
2019	J	`jnl`	Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. CoRR Pengzhan Jin, Lu Lu, Yifa Tang, George E. Karniadakis