An Adaptive Non-Intrusive Multi-Fidelity Reduced Basis Method for Parameterized Partial Differential Equations (Q6110109)

scientific article; zbMATH DE number 7707320

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English	An Adaptive Non-Intrusive Multi-Fidelity Reduced Basis Method for Parameterized Partial Differential Equations	scientific article; zbMATH DE number 7707320

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instance of

scholarly article

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title

An Adaptive Non-Intrusive Multi-Fidelity Reduced Basis Method for Parameterized Partial Differential Equations (English)

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East Asian Journal on Applied Mathematics

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publication date

4 July 2023

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zbMATH Keywords

multi-fidelity method

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non-intrusive

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reduced-order model

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Gaussian process regression

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adaptive sampling

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MaRDI profile type

MaRDI publication profile

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full work available at URL

https://doi.org/10.4208/eajam.2022-244.241022

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cites work

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Supremizer stabilization of POD-Galerkin approximation of parametrized steady incompressible Navier-Stokes equations

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Comparison between reduced basis and stochastic collocation methods for elliptic problems

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Non-Intrusive Reduced Order Modeling of Convection Dominated Flows Using Artificial Neural Networks with Application to Rayleigh-Taylor Instability

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Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space

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A Gaussian process regression approach within a data-driven POD framework for engineering problems in fluid dynamics

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A posteriorierror bounds for reduced-basis approximations of parametrized parabolic partial differential equations

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Reduced basis method for finite volume approximations of parametrized linear evolution equations

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Certified Reduced Basis Methods for Parametrized Partial Differential Equations

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

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Stability properties of POD-Galerkin approximations for the compressible Navier-Stokes equations

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A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

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Predicting the output from a complex computer code when fast approximations are available

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Galerkin proper orthogonal decomposition methods for parabolic problems

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The Finite Element Method: Theory, Implementation, and Applications

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PROPER ORTHOGONAL DECOMPOSITION AND ITS APPLICATIONS—PART I: THEORY

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Bifidelity data-assisted neural networks in nonintrusive reduced-order modeling

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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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A MULTI-FIDELITY NEURAL NETWORK SURROGATE SAMPLING METHOD FOR UNCERTAINTY QUANTIFICATION

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A Stochastic Collocation Algorithm with Multifidelity Models

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Inferring solutions of differential equations using noisy multi-fidelity data

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Model reduction for compressible flows using POD and Galerkin projection

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Certified real‐time solution of the parametrized steady incompressible Navier–Stokes equations: rigorous reduced‐basis a posteriori error bounds

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A PosterioriError Estimation for Reduced-Basis Approximation of Parametrized Elliptic Coercive Partial Differential Equations: “Convex Inverse” Bound Conditioners

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A parameterized non-intrusive reduced order model and error analysis for general time-dependent nonlinear partial differential equations and its applications

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Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems

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An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems

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When Bifidelity Meets CoKriging: An Efficient Physics-Informed MultiFidelity Method

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Adaptive non-intrusive reduced order modeling for compressible flows

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Multi-fidelity stochastic collocation method for computation of statistical moments

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Identifiers

zbMATH Open document ID

1514.65125

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DOI

10.4208/eajam.2022-244.241022

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:6110109