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This paper establishes a connection between non-convex optimization and nonlinear partial differential equations (PDEs).
Apr 17, 2017 · In this paper we establish a connection between non-convex optimization methods for training deep neural networks and nonlinear partial differential equations ...
Mar 18, 2023 · An innovative method is introduced in this study to solve linear equations based on deep neural networks. To achieve a high accuracy, we employ the residual ...
Oct 27, 2022 · This survey aims to categorize and review the current progress on Deep Neural Networks (DNNs) for PDEs.
Missing: training | Show results with:training
Partial differential equations (PDEs) are among the most ubiquitous tools used in modeling problems in nature. However, solving high-dimensional PDEs has ...
In this thesis three different approaches for solving partial differential equa- tions with neural networks will be explored; namely Physics-Informed Neural.
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Dec 8, 2021 · Deep neural networks (DNNs) have recently shown great potential in solving partial differential equations (PDEs). The success of neural ...
Dec 12, 2023 · The study of physical systems, modeled by partial differential equations (PDEs), represents a cornerstone of scientific research.
In this project we derived and proposed a coupled deep learning neural network for solving the Laplace problem in two and higher dimensions. Numerical results.
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Apr 19, 2021 · Two new approaches allow deep neural networks to solve entire families of partial differential equations, making it easier to model ...
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