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Adaptive transfer learning for PINN (Q6173323)

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scientific article; zbMATH DE number 7715226

Language	Label	Description	Also known as
English	Adaptive transfer learning for PINN	scientific article; zbMATH DE number 7715226

Statements

scholarly article

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Adaptive transfer learning for PINN (English)

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Journal of Computational Physics

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publication date

21 July 2023

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zbMATH Keywords

physical-informed neural networks

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transfer learning

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adaptive learning

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non-convex problem

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the minimum energy path

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MaRDI profile type

MaRDI publication profile

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Deep learning in fluid dynamics

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Design and optimization of artificial neural networks for the modelling of superconducting magnets operation in tokamak fusion reactors

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Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

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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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Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

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Physics-informed neural networks for inverse problems in supersonic flows

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Physics-informed neural networks for the shallow-water equations on the sphere

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A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations

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Explicit physics-informed neural networks for nonlinear closure: the case of transport in tissues

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

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Stable \textit{a posteriori} LES of 2D turbulence using convolutional neural networks: backscattering analysis and generalization to higher \(Re\) via transfer learning

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

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Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation

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Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture

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

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Transfer learning based multi-fidelity physics informed deep neural network

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Optimally weighted loss functions for solving PDEs with neural networks

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An adaptive high-order minimum action method

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A Limited Memory Algorithm for Bound Constrained Optimization

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Identifiers

Mathematics Subject Classification ID

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zbMATH DE Number

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10.1016/J.JCP.2023.112291

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Sitelinks

Mathematics(1 entry)

mardi Publication:6173323

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