Pengzhan Jin | DeepAI

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Featured Co-authors

George Em Karniadakis
94 publications
Zhen Zhang
59 publications
Jun Hu
49 publications
Lu Lu
44 publications
Ioannis G. Kevrekidis
42 publications
Yifa Tang
15 publications
Aiqing Zhu
13 publications
Beibei Zhu
7 publications
Shuai Meng
1 publication

∙ 02/01/2023

Experimental observation on a low-rank tensor model for eigenvalue problems

Here we utilize a low-rank tensor model (LTM) as a function approximator...

0 Jun Hu, et al. ∙

∙ 06/15/2022

On Numerical Integration in Neural Ordinary Differential Equations

The combination of ordinary differential equations and neural networks, ...

0 Aiqing Zhu, et al. ∙

∙ 02/12/2022

MIONet: Learning multiple-input operators via tensor product

As an emerging paradigm in scientific machine learning, neural operators...

0 Pengzhan Jin, et al. ∙

∙ 06/21/2021

Approximation capabilities of measure-preserving neural networks

Measure-preserving neural networks are well-developed invertible models,...

0 Aiqing Zhu, et al. ∙

∙ 12/05/2020

Learning Poisson systems and trajectories of autonomous systems via Poisson neural networks

We propose the Poisson neural networks (PNNs) to learn Poisson systems a...

12 Pengzhan Jin, et al. ∙

∙ 09/02/2020

Inverse modified differential equations for discovery of dynamics

We introduce inverse modified differential equations (IMDEs) to contribu...

0 Aiqing Zhu, et al. ∙

∙ 04/23/2020

Deep Hamiltonian networks based on symplectic integrators

HNets is a class of neural networks on grounds of physical prior for lea...

0 Aiqing Zhu, et al. ∙

∙ 01/11/2020

Symplectic networks: Intrinsic structure-preserving networks for identifying Hamiltonian systems

This work presents a framework of constructing the neural networks prese...

5 Pengzhan Jin, et al. ∙

∙ 10/08/2019

DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators

While it is widely known that neural networks are universal approximator...

31 Lu Lu, et al. ∙

∙ 05/27/2019

Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness

The accuracy of deep learning, i.e., deep neural networks, can be charac...

31 Pengzhan Jin, et al. ∙

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