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Variable linear transformation improved physics-informed neural networks to solve thin-layer flow problems - MaRDI portal

Variable linear transformation improved physics-informed neural networks to solve thin-layer flow problems (Q6119307)

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scientific article; zbMATH DE number 7811343

Language	Label	Description	Also known as
English	Variable linear transformation improved physics-informed neural networks to solve thin-layer flow problems	scientific article; zbMATH DE number 7811343

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scholarly article

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Variable linear transformation improved physics-informed neural networks to solve thin-layer flow problems (English)

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10.1016/j.jcp.2024.112761

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Journal of Computational Physics

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publication date

29 February 2024

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Mathematics Subject Classification ID

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zbMATH DE Number

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zbMATH Keywords

physics-informed neural networks (PINNs)

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differential equations (DEs)

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jet flow

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wake flow

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mixing layer

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boundary layer

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MaRDI profile type

MaRDI publication profile

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full work available at URL

https://doi.org/10.1016/j.jcp.2024.112761

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

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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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DeepXDE: A Deep Learning Library for Solving Differential Equations

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Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks

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NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations

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Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations

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PPINN: parareal physics-informed neural network for time-dependent PDEs

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Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems

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Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

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A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics

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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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PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs

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Physics-informed multi-LSTM networks for metamodeling of nonlinear structures

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Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

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Physics-Informed Neural Networks with Hard Constraints for Inverse Design

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Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks

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Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation

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Boundary-Layer Theory

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THE VELOCITY DISTRIBUTION IN THE LAMINAR BOUNDARY LAYER BETWEEN PARALLEL STREAMS

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Sitelinks

Mathematics(1 entry)

mardi Publication:6119307

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