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

DOI10.1016/j.cma.2021.114378OpenAlexW3133657579MaRDI QIDQ2072477

Andrea Manzoni, Paolo Conti, Maurice Amendt, Mengwu Guo, Jan S. Hesthaven

Publication date: 26 January 2022

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2102.13403

zbMATH Keywords

machine learningartificial neural networkreduced order modelingGaussian process regressionparametrized PDEmulti-fidelity regression

Mathematics Subject Classification ID

Numerical computation using splines (65D07) Artificial neural networks and deep learning (68T07) Neural networks for/in biological studies, artificial life and related topics (92B20) Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs (65M60) Numerical radial basis function approximation (65D12)

Related Items

Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression ⋮ Multi-fidelity surrogate modeling using long short-term memory networks ⋮ Mesh-informed neural networks for operator learning in finite element spaces ⋮ Parametric reduced-order modeling enhancement for a geometrically imperfect component via hyper-reduction ⋮ An extended physics informed neural network for preliminary analysis of parametric optimal control problems ⋮ A novel sampling method for adaptive gradient-enhanced kriging ⋮ Enhanced kriging leave-one-out cross-validation in improving model estimation and optimization ⋮ Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations ⋮ Deep learning-enhanced efficient seismic analysis of structures with multi-fidelity modeling strategies ⋮ Multifidelity data fusion in convolutional encoder/decoder networks

Uses Software

• redbKIT
• GPy
• Hyperopt
• GitHub
• DGM

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