Nov 15, 2021 · In this paper, we propose Meta-Auto-Decoder (MAD), a mesh-free and unsupervised deep learning method that enables the pre-trained model to be ...
This kind of approaches approximate the solution of the PDEs with a neural network and mainly rely on governing equations and boundary conditions (or their.
NN as a new ansatz of solution Approximating the solution of the PDEs with a neural network. PDEs or their variant forms are used as loss terms for.
Feb 16, 2023 · Meta-Auto-Decoder: A Meta-Learning Based Reduced Order Model for Solving Parametric Partial Differential Equations. Authors:Zhanhong Ye, Xiang ...
Aug 14, 2023 · We propose the Meta-Auto-Decoder (MAD), a mesh-free and unsupervised deep learning method that enables the pre-trained model to be quickly ...
Apr 3, 2024 · In this paper, we propose Meta-Auto-Decoder (MAD), a mesh-free and unsupervised deep learning method that enables the pre-trained model to be ...
Meta-Auto-Decoder (MAD), a mesh-free and unsupervised deep learning method that enables the pre-trained model to be quickly adapted to equation instances by ...
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Jan 26, 2024 · Meta-Auto-Decoder: A Meta-Learning-Based Reduced Order Model for Solving Parametric Partial Differential Equations ; Background Meta-Auto-Decoder ...
Nov 15, 2021 · In these applications, our goal is to solve parametric PDEs rather than one instance of them. Our proposed approach, called Meta-Auto-Decoder ( ...
Apr 25, 2024 · Meta-Auto-Decoder for Solving Parametric Partial Differential Equations. ... Auto-Decoder for Solving Parametric Partial Differential Equations ...