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Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh

Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh

3 November 2022
Tian Qin
Alex Beatson
Deniz Oktay
N. McGreivy
Ryan P. Adams
    AI4CE
ArXivPDFHTML

Papers citing "Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh"

12 / 12 papers shown
Title
Enabling Automatic Differentiation with Mollified Graph Neural Operators
Enabling Automatic Differentiation with Mollified Graph Neural Operators
Ryan Y. Lin
Julius Berner
Valentin Duruisseaux
David Pitt
Daniel Leibovici
Jean Kossaifi
Kamyar Azizzadenesheli
Anima Anandkumar
AI4CE
43
0
0
11 Apr 2025
Adaptive Physics-informed Neural Networks: A Survey
Adaptive Physics-informed Neural Networks: A Survey
Edgar Torres
Jonathan Schiefer
Mathias Niepert
PINN
AI4CE
63
0
0
23 Mar 2025
Weak baselines and reporting biases lead to overoptimism in machine
  learning for fluid-related partial differential equations
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
N. McGreivy
Ammar Hakim
AI4CE
29
42
0
09 Jul 2024
Meta-learning of Physics-informed Neural Networks for Efficiently
  Solving Newly Given PDEs
Meta-learning of Physics-informed Neural Networks for Efficiently Solving Newly Given PDEs
Tomoharu Iwata
Yusuke Tanaka
N. Ueda
AI4CE
13
2
0
20 Oct 2023
Physics-aware Machine Learning Revolutionizes Scientific Paradigm for
  Machine Learning and Process-based Hydrology
Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology
Qingsong Xu
Yilei Shi
Jonathan Bamber
Ye Tuo
Ralf Ludwig
Xiao Xiang Zhu
AI4CE
18
9
0
08 Oct 2023
Meta-Learning for Airflow Simulations with Graph Neural Networks
Meta-Learning for Airflow Simulations with Graph Neural Networks
Wenzhuo Liu
Mouadh Yagoubi
Marc Schoenauer
AI4CE
19
0
0
18 Jun 2023
About optimal loss function for training physics-informed neural
  networks under respecting causality
About optimal loss function for training physics-informed neural networks under respecting causality
V. A. Es'kin
Danil V. Davydov
Ekaterina D. Egorova
Alexey O. Malkhanov
Mikhail A. Akhukov
Mikhail E. Smorkalov
PINN
16
7
0
05 Apr 2023
GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward
  non-intrusive Meta-learning of parametric PDEs
GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs
Yanlai Chen
Shawn Koohy
PINN
AI4CE
23
24
0
27 Mar 2023
Efficient physics-informed neural networks using hash encoding
Efficient physics-informed neural networks using hash encoding
Xinquan Huang
T. Alkhalifah
17
28
0
26 Feb 2023
Meta-learning PINN loss functions
Meta-learning PINN loss functions
Apostolos F. Psaros
Kenji Kawaguchi
George Karniadakis
PINN
35
97
0
12 Jul 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
203
2,282
0
18 Oct 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
281
11,681
0
09 Mar 2017
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