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Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs

Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs

Thomas O'Leary-Roseberry
Umberto Villa
Peng Chen
Omar Ghattas
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Papers citing "Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs"

10 / 10 papers shown
Title
Generalization Error Guaranteed Auto-Encoder-Based Nonlinear Model Reduction for Operator Learning
Generalization Error Guaranteed Auto-Encoder-Based Nonlinear Model Reduction for Operator Learning
Hao Liu
Biraj Dahal
Rongjie Lai
Wenjing Liao
AI4CE
34
5
0
19 Jan 2024
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks
Sanket R. Jantre
Nathan M. Urban
Xiaoning Qian
Byung-Jun Yoon
BDL
UQCV
23
4
0
06 Sep 2023
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning
Thomas O'Leary-Roseberry
Peng Chen
Umberto Villa
Omar Ghattas
AI4CE
32
39
0
21 Jun 2022
Learning High-Dimensional Parametric Maps via Reduced Basis Adaptive Residual Networks
Learning High-Dimensional Parametric Maps via Reduced Basis Adaptive Residual Networks
Thomas O'Leary-Roseberry
Xiaosong Du
A. Chaudhuri
J. Martins
Karen E. Willcox
Omar Ghattas
30
22
0
14 Dec 2021
slimTrain -- A Stochastic Approximation Method for Training Separable Deep Neural Networks
slimTrain -- A Stochastic Approximation Method for Training Separable Deep Neural Networks
Elizabeth Newman
Julianne Chung
Matthias Chung
Lars Ruthotto
47
6
0
28 Sep 2021
Convergence Rates for Learning Linear Operators from Noisy Data
Convergence Rates for Learning Linear Operators from Noisy Data
Maarten V. de Hoop
Nikola B. Kovachki
Nicholas H. Nelsen
Andrew M. Stuart
19
54
0
27 Aug 2021
Neural Operator: Learning Maps Between Function Spaces
Neural Operator: Learning Maps Between Function Spaces
Nikola B. Kovachki
Zong-Yi Li
Burigede Liu
Kamyar Azizzadenesheli
K. Bhattacharya
Andrew M. Stuart
Anima Anandkumar
AI4CE
37
440
0
19 Aug 2021
Projected Stein Variational Gradient Descent
Projected Stein Variational Gradient Descent
Peng Chen
Omar Ghattas
BDL
55
68
0
09 Feb 2020
Geometric deep learning on graphs and manifolds using mixture model CNNs
Geometric deep learning on graphs and manifolds using mixture model CNNs
Federico Monti
Davide Boscaini
Jonathan Masci
Emanuele Rodolà
Jan Svoboda
M. Bronstein
GNN
251
1,811
0
25 Nov 2016
Geometric deep learning: going beyond Euclidean data
Geometric deep learning: going beyond Euclidean data
M. Bronstein
Joan Bruna
Yann LeCun
Arthur Szlam
P. Vandergheynst
GNN
259
3,239
0
24 Nov 2016