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Multifidelity modeling for physics-informed neural networks (PINNs) (Q2134766)

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Language	Label	Description	Also known as
English	Multifidelity modeling for physics-informed neural networks (PINNs)	scientific article

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scholarly article

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Multifidelity modeling for physics-informed neural networks (PINNs) (English)

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Michael Penwarden

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Akil C. Narayan

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Robert M. Kirby

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Journal of Computational Physics

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publication date

3 May 2022

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full work available at URL

https://arxiv.org/abs/2106.13361

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zbMATH Keywords

physics-informed neural networks (PINNs)

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multifidelity

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surrogate modeling

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reduced-order modeling

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describes a project that uses

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MaRDI profile type

MaRDI publication profile

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Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization

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Convergence Acceleration for Time-Dependent Parametric Multifidelity Models

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A Stochastic Collocation Algorithm with Multifidelity Models

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Computational Aspects of Stochastic Collocation with Multifidelity Models

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Practical error bounds for a non-intrusive bi-fidelity approach to parametric/stochastic model reduction

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Fast predictive multi-fidelity prediction with models of quantized fidelity levels

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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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KERNEL OPTIMIZATION FOR LOW-RANK MULTIFIDELITY ALGORITHMS

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Allocation Strategies for High Fidelity Models in the Multifidelity Regime

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A radial basis function (RBF) finite difference method for the simulation of reaction–diffusion equations on stationary platelets within the augmented forcing method

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A radial basis function (RBF)-finite difference (FD) method for diffusion and reaction-diffusion equations on surfaces

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Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems

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PPINN: parareal physics-informed neural network for time-dependent PDEs

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

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fPINNs: Fractional Physics-Informed Neural Networks

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nPINNs: nonlocal physics-informed neural networks for a parametrized nonlocal universal Laplacian operator. Algorithms and applications

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

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Spectral and finite difference solutions of the Burgers equations

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Identifiers

10.1016/j.jcp.2021.110844

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Mathematics Subject Classification ID

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zbMATH DE Number

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Sitelinks

Mathematics(1 entry)

mardi Publication:2134766

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