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Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness
27 May 2019
Pengzhan Jin
Lu Lu
Yifa Tang
George Karniadakis
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Papers citing
"Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness"
16 / 16 papers shown
Title
Reliable extrapolation of deep neural operators informed by physics or sparse observations
Min Zhu
Handi Zhang
Anran Jiao
George Karniadakis
Lu Lu
106
100
0
13 Dec 2022
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
Brian Bartoldson
B. Kailkhura
Davis W. Blalock
109
51
0
13 Oct 2022
Certified machine learning: Rigorous a posteriori error bounds for PDE defined PINNs
Birgit Hillebrecht
B. Unger
PINN
84
5
0
07 Oct 2022
Overview frequency principle/spectral bias in deep learning
Z. Xu
Yaoyu Zhang
Yaoyu Zhang
FaML
92
74
0
19 Jan 2022
Embedding Principle: a hierarchical structure of loss landscape of deep neural networks
Yaoyu Zhang
Yuqing Li
Zhongwang Zhang
Yaoyu Zhang
Z. Xu
86
23
0
30 Nov 2021
Approximation capabilities of measure-preserving neural networks
Aiqing Zhu
Pengzhan Jin
Yifa Tang
84
8
0
21 Jun 2021
Embedding Principle of Loss Landscape of Deep Neural Networks
Yaoyu Zhang
Zhongwang Zhang
Yaoyu Zhang
Z. Xu
69
38
0
30 May 2021
Mosaic Flows: A Transferable Deep Learning Framework for Solving PDEs on Unseen Domains
Hengjie Wang
R. Planas
Aparna Chandramowlishwaran
Ramin Bostanabad
AI4CE
149
64
0
22 Apr 2021
On Theory-training Neural Networks to Infer the Solution of Highly Coupled Differential Equations
M. T. Rad
A. Viardin
M. Apel
AI4CE
42
3
0
09 Feb 2021
Physics-informed neural networks with hard constraints for inverse design
Lu Lu
R. Pestourie
Wenjie Yao
Zhicheng Wang
F. Verdugo
Steven G. Johnson
PINN
102
522
0
09 Feb 2021
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE
Juntang Zhuang
Nicha Dvornek
Xiaoxiao Li
S. Tatikonda
X. Papademetris
James Duncan
BDL
127
112
0
03 Jun 2020
SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems
Pengzhan Jin
Zhen Zhang
Aiqing Zhu
Yifa Tang
George Karniadakis
105
21
0
11 Jan 2020
Deep learning architectures for nonlinear operator functions and nonlinear inverse problems
Maarten V. de Hoop
Matti Lassas
C. Wong
78
26
0
23 Dec 2019
Variational Physics-Informed Neural Networks For Solving Partial Differential Equations
E. Kharazmi
Z. Zhang
George Karniadakis
108
246
0
27 Nov 2019
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
Lu Lu
Pengzhan Jin
George Karniadakis
254
2,179
0
08 Oct 2019
DeepXDE: A deep learning library for solving differential equations
Lu Lu
Xuhui Meng
Zhiping Mao
George Karniadakis
PINN
AI4CE
101
1,555
0
10 Jul 2019
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