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Deep transfer operator learning for partial differential equations under
  conditional shift

Deep transfer operator learning for partial differential equations under conditional shift

20 April 2022
S. Goswami
Katiana Kontolati
Michael D. Shields
George Karniadakis
ArXivPDFHTML

Papers citing "Deep transfer operator learning for partial differential equations under conditional shift"

11 / 11 papers shown
Title
A Physics-preserved Transfer Learning Method for Differential Equations
A Physics-preserved Transfer Learning Method for Differential Equations
Hao-Ran Yang
Chuan-Xian Ren
143
0
0
02 May 2025
Using Linearized Optimal Transport to Predict the Evolution of Stochastic Particle Systems
Using Linearized Optimal Transport to Predict the Evolution of Stochastic Particle Systems
Nicholas Karris
Evangelos A. Nikitopoulos
Ioannis G. Kevrekidis
Seungjoon Lee
Alexander Cloninger
OT
45
0
0
10 Jan 2025
Strategies for Pretraining Neural Operators
Strategies for Pretraining Neural Operators
Anthony Y. Zhou
Cooper Lorsung
AmirPouya Hemmasian
Amir Barati Farimani
AI4CE
39
5
0
12 Jun 2024
Learning solution operators of PDEs defined on varying domains via
  MIONet
Learning solution operators of PDEs defined on varying domains via MIONet
Shanshan Xiao
Pengzhan Jin
Yifa Tang
47
3
0
23 Feb 2024
In-Context Operator Learning with Data Prompts for Differential Equation
  Problems
In-Context Operator Learning with Data Prompts for Differential Equation Problems
Liu Yang
Siting Liu
Tingwei Meng
Stanley J. Osher
40
59
0
17 Apr 2023
Neural Operator Learning for Long-Time Integration in Dynamical Systems
  with Recurrent Neural Networks
Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks
K. Michałowska
S. Goswami
George Karniadakis
S. Riemer-Sørensen
AI4CE
15
15
0
03 Mar 2023
L-HYDRA: Multi-Head Physics-Informed Neural Networks
L-HYDRA: Multi-Head Physics-Informed Neural Networks
Zongren Zou
George Karniadakis
AI4CE
18
26
0
05 Jan 2023
Explaining the physics of transfer learning a data-driven subgrid-scale
  closure to a different turbulent flow
Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow
Adam Subel
Yifei Guan
A. Chattopadhyay
P. Hassanzadeh
AI4CE
29
41
0
07 Jun 2022
Towards Size-Independent Generalization Bounds for Deep Operator Nets
Towards Size-Independent Generalization Bounds for Deep Operator Nets
Pulkit Gopalani
Sayar Karmakar
Dibyakanti Kumar
Anirbit Mukherjee
AI4CE
24
5
0
23 May 2022
Mosaic Flows: A Transferable Deep Learning Framework for Solving PDEs on
  Unseen Domains
Mosaic Flows: A Transferable Deep Learning Framework for Solving PDEs on Unseen Domains
Hengjie Wang
R. Planas
Aparna Chandramowlishwaran
Ramin Bostanabad
AI4CE
50
61
0
22 Apr 2021
Fourier Neural Operator for Parametric Partial Differential Equations
Fourier Neural Operator for Parametric Partial Differential Equations
Zong-Yi Li
Nikola B. Kovachki
Kamyar Azizzadenesheli
Burigede Liu
K. Bhattacharya
Andrew M. Stuart
Anima Anandkumar
AI4CE
217
2,287
0
18 Oct 2020
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