Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets (Q6159313)

scientific article; zbMATH DE number 7691246

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English	Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets	scientific article; zbMATH DE number 7691246

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instance of

scholarly article

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title

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

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Computational Mechanics

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publication date

1 June 2023

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full work available at URL

https://arxiv.org/abs/2204.00997

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zbMATH Keywords

neural network

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deep operator network

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uncertainty quantification

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low-fidelity system response

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nonlinear steady-state heat transfer

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thin plate

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Duffing oscillator

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wind farm model

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MaRDI profile type

MaRDI publication profile

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cites work

Large-Scale Machine Learning with Stochastic Gradient Descent

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DeepM\&Mnet: inferring the electroconvection multiphysics fields based on operator approximation by neural networks

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

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

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Bi-fidelity stochastic gradient descent for structural optimization under uncertainty

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A non-adapted sparse approximation of PDEs with stochastic inputs

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Turbulence Modeling in the Age of Data

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Multi-fidelity optimization via surrogate modelling

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Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks

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Bayesian Calibration of Computer Models

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Q5053337

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Error estimates for DeepONets: a deep learning framework in infinite dimensions

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Automated solution of differential equations by the finite element method. The FEniCS book

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A paradigm for data-driven predictive modeling using field inversion and machine learning

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Turbulent Flows

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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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Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification

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

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Q3394879

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ConvPDE-UQ: convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains

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The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations

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Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

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B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

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Adversarial uncertainty quantification in physics-informed neural networks

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Error bounds for approximations with deep ReLU networks

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Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

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Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

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Identifiers

zbMATH Open document ID

1521.74407

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arXiv ID

2204.00997

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Mathematics Subject Classification ID

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10.1007/S00466-023-02272-4

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Sitelinks

Mathematics(1 entry)

mardi Publication:6159313