Multifidelity deep operator networks for data-driven and physics-informed problems (Q6048427)

scientific article; zbMATH DE number 7748048

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English	Multifidelity deep operator networks for data-driven and physics-informed problems	scientific article; zbMATH DE number 7748048

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instance of

scholarly article

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title

Multifidelity deep operator networks for data-driven and physics-informed problems (English)

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arXiv ID

2204.09157

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DOI

10.1016/j.jcp.2023.112462

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author

Amanda A. Howard

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Mauro Perego

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George Em. Karniadakis

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Panos Stinis

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published in

Journal of Computational Physics

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publication date

10 October 2023

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full work available at URL

https://arxiv.org/abs/2204.09157

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Mathematics Subject Classification ID

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neural operator

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multifidelity

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operator learning

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physics-informed machine learning

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ice-sheet dynamics

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MaRDI profile type

MaRDI publication profile

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Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling

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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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Nonlocal kernel network (NKN): a stable and resolution-independent deep neural network

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Universal Approximation of Multiple Nonlinear Operators by Neural Networks

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A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials

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A seamless multiscale operator neural network for inferring bubble dynamics

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Improved architectures and training algorithms for deep operator networks

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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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DGM: a deep learning algorithm for solving partial differential equations

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Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks

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

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Q3474197

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Q4558473

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

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Multifidelity modeling for physics-informed neural networks (PINNs)

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

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Neural network training using \(\ell_1\)-regularization and bi-fidelity data

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Transfer learning based multi-fidelity physics informed deep neural network

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A fast multi-fidelity method with uncertainty quantification for complex data correlations: application to vortex-induced vibrations of marine risers

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Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization

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Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets

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Self-adaptive physics-informed neural networks

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A hybrid deep neural operator/finite element method for ice-sheet modeling

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A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems, Part II: Stochastic Newton MCMC with Application to Ice Sheet Flow Inverse Problems

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Exponential time differencing for stiff systems

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A multifidelity deep operator network approach to closure for multiscale systems

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Mathematics(1 entry)

mardi Publication:6048427