Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations (Q6187659)

scientific article; zbMATH DE number 7797662

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English	Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations	scientific article; zbMATH DE number 7797662

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instance of

scholarly article

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title

Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations (English)

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Journal of Computational Physics

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publication date

31 January 2024

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full work available at URL

https://arxiv.org/abs/2303.11577

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zbMATH Keywords

multi-fidelity

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physics-informed

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machine learning

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feature space

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MaRDI profile type

MaRDI publication profile

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cites work

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

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Deep learning of vortex-induced vibrations

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

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Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks

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On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks

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Failure-Informed Adaptive Sampling for PINNs

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DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations

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A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

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

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A composite neural network that learns from multi-fidelity data: application to function approximation and inverse PDE problems

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Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities

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A physics-informed multi-fidelity approach for the estimation of differential equations parameters in low-data or large-noise regimes

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Multi-fidelity Bayesian neural networks: algorithms and applications

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Transfer learning based multi-fidelity physics informed deep neural network

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Use of multifidelity training data and transfer learning for efficient construction of subsurface flow surrogate models

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ON TRANSFER LEARNING OF NEURAL NETWORKS USING BI-FIDELITY DATA FOR UNCERTAINTY PROPAGATION

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Physics-informed machine learning for reduced-order modeling of nonlinear problems

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Physics and equality constrained artificial neural networks: application to forward and inverse problems with multi-fidelity data fusion

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Non-intrusive reduced order modeling of nonlinear problems using neural networks

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Q5491033

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On the limited memory BFGS method for large scale optimization

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The treatment of spurious pressure modes in spectral incompressible flow calculations

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The 2D lid-driven cavity problem revisited

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Long-time integration of parametric evolution equations with physics-informed DeepONets

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Identifiers

arXiv ID

2303.11577

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DOI

10.1016/j.jcp.2023.112683

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:6187659