Feb 16, 2023 · Abstract:Many important problems in science and engineering require solving the so-called parametric partial differential equations (PDEs), ...
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 ...
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 ...
We propose Meta-Auto-Decoder (MAD), a mesh-free and unsupervised deep learning method that enables the pre-trained model to be quickly adapted to equation ...
Jan 26, 2024 · Meta-Auto-Decoder: A Meta-Learning-Based Reduced Order Model for Solving Parametric Partial Differential Equations ; loosened assumption: The ...
Feb 16, 2023 · Utilizing the nonlinear representation of neural networks, we propose Meta-Auto-Decoder (MAD) to construct a nonlinear trial manifold, whose ...
Abstract. Many important problems in science and engineering require solving the so-called parametric partial differential equations (PDEs), i.e., PDEs with.
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: a Meta-Learning-Based Reduced Order Model for Solving Parametric Partial Differential Equations ; Journal: Communications on Applied ...
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.
Missing: Reduced Order