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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
Authors:  Zhu, Aiqing;  Jin, Pengzhan;  Tang, Yifa
Favorite  |  View/Download:113/0  |  Submit date:2022/06/21
Measure-preserving  Neural networks  Dynamical systems  Approximation theory  
SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems 期刊论文
NEURAL NETWORKS, 2020, 卷号: 132, 页码: 166-179
Authors:  Jin, Pengzhan;  Zhang, Zhen;  Zhu, Aiqing;  Tang, Yifa;  Karniadakis, George Em
Favorite  |  View/Download:143/0  |  Submit date: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
Authors:  Jin, Pengzhan;  Lu, Lu;  Tang, Yifa;  Karniadakis, George Em
Favorite  |  View/Download:118/0  |  Submit date:2021/01/14
Neural networks  Generalization error  Learnability  Data distribution  Cover complexity  Neural network smoothness