A short note on solving partial differential equations using convolutional neural networks (Q6620254)

scientific article; zbMATH DE number 7927646

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English	A short note on solving partial differential equations using convolutional neural networks	scientific article; zbMATH DE number 7927646

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title

A short note on solving partial differential equations using convolutional neural networks (English)

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16 October 2024

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Publication

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cites work

Scientific machine learning through physics-informed neural networks: where we are and what's next

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Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs

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Stationary Flow Predictions Using Convolutional Neural Networks

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Surrogate convolutional neural network models for steady computational fluid dynamics simulations

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A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs

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On lines and planes of closest fit to systems of points in space.

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PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain

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Q5270493

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Proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant diffusion and nonlinear drift coefficients

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Model Order Reduction in Fluid Dynamics: Challenges and Perspectives

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On the limited memory BFGS method for large scale optimization

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Reduced Basis Methods for Partial Differential Equations

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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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A New Look at Proper Orthogonal Decomposition

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When and why PINNs fail to train: a neural tangent kernel perspective

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Identifiers

DOI

10.1007/978-3-031-50769-4_1

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Mathematics(1 entry)

mardi Publication:6620254