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ORCIDYifa Tang
Publications
- 2024
[i18]Shanshan Xiao, Pengzhan Jin, Yifa Tang:
Learning solution operators of PDEs defined on varying domains via MIONet. CoRR abs/2402.15097 (2024)- [i16]Shanshan Xiao, Pengzhan Jin, Yifa Tang:
A deformation-based framework for learning solution mappings of PDEs defined on varying domains. CoRR abs/2412.01379 (2024) - 2022
- [j42]Aiqing Zhu
, Pengzhan Jin, Yifa Tang:
Approximation capabilities of measure-preserving neural networks. Neural Networks 147: 72-80 (2022) - [c2]Aiqing Zhu, Pengzhan Jin, Beibei Zhu, Yifa Tang:
On Numerical Integration in Neural Ordinary Differential Equations. ICML 2022: 27527-27547 - [i9]Aiqing Zhu, Pengzhan Jin, Beibei Zhu, Yifa Tang:
On Numerical Integration in Neural Ordinary Differential Equations. CoRR abs/2206.07335 (2022) - 2021
- [i6]Aiqing Zhu, Pengzhan Jin, Yifa Tang:
Approximation capabilities of measure-preserving neural networks. CoRR abs/2106.10911 (2021) - 2020
- [j35]Pengzhan Jin, Lu Lu, Yifa Tang, George Em Karniadakis:
Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. Neural Networks 130: 85-99 (2020) - [j34]Pengzhan Jin, Zhen Zhang, Aiqing Zhu, Yifa Tang, George Em Karniadakis:
SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems. Neural Networks 132: 166-179 (2020) - [j33]Pengzhan Jin, Yifa Tang, Aiqing Zhu:
Unit Triangular Factorization of the Matrix Symplectic Group. SIAM J. Matrix Anal. Appl. 41(4): 1630-1650 (2020) - [i5]Pengzhan Jin, Aiqing Zhu, George Em Karniadakis, Yifa Tang:
Symplectic networks: Intrinsic structure-preserving networks for identifying Hamiltonian systems. CoRR abs/2001.03750 (2020) - [i4]Aiqing Zhu, Pengzhan Jin, Yifa Tang:
Deep Hamiltonian networks based on symplectic integrators. CoRR abs/2004.13830 (2020) - [i3]Aiqing Zhu, Pengzhan Jin, Yifa Tang:
Inverse modified differential equations for discovery of dynamics. CoRR abs/2009.01058 (2020) - 2019
- [i2]Pengzhan Jin, Lu Lu, Yifa Tang, George E. Karniadakis:
Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. CoRR abs/1905.11427 (2019)
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