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Approximation capabilities of measure-preserving neural networks 期刊论文
NEURAL NETWORKS, 2022, 卷号: 147, 页码: 72-80
作者:  Zhu, Aiqing;  Jin, Pengzhan;  Tang, Yifa
收藏  |  浏览/下载：112/0  |  提交时间：2022/06/21
Measure-preserving  Neural networks  Dynamical systems  Approximation theory  
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators 期刊论文
NATURE MACHINE INTELLIGENCE, 2021, 卷号: 3, 期号: 3, 页码: 218-+
作者:  Lu, Lu;  Jin, Pengzhan;  Pang, Guofei;  Zhang, Zhongqiang;  Karniadakis, George Em
收藏  |  浏览/下载：202/0  |  提交时间：2021/06/01
SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems 期刊论文
NEURAL NETWORKS, 2020, 卷号: 132, 页码: 166-179
作者:  Jin, Pengzhan;  Zhang, Zhen;  Zhu, Aiqing;  Tang, Yifa;  Karniadakis, George Em
收藏  |  浏览/下载：142/0  |  提交时间：2021/04/26
Deep learning  Physics-informed  Dynamical systems  Hamiltonian systems  Symplectic maps  Symplectic integrators  
Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness 期刊论文
NEURAL NETWORKS, 2020, 卷号: 130, 页码: 85-99
作者:  Jin, Pengzhan;  Lu, Lu;  Tang, Yifa;  Karniadakis, George Em
收藏  |  浏览/下载：117/0  |  提交时间：2021/01/14
Neural networks  Generalization error  Learnability  Data distribution  Cover complexity  Neural network smoothness  
UNIT TRIANGULAR FACTORIZATION OF THE MATRIX SYMPLECTIC GROUP 期刊论文
SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 2020, 卷号: 41, 期号: 4, 页码: 1630-1650
作者:  Jin, Pengzhan;  Tang, Yifa;  Zhu, Aiqing
收藏  |  浏览/下载：115/0  |  提交时间：2021/04/26
symplectic  symplectic matrix  triangular factorization  positive definite  singular symplectic matrix  generators  parameterization  unconstrained parametrization