Error estimation of the parametric non-intrusive reduced order model using machine learning
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Publication:1988235
DOI10.1016/j.cma.2019.06.018zbMath1441.62081OpenAlexW2954760057WikidataQ127563349 ScholiaQ127563349MaRDI QIDQ1988235
Publication date: 16 April 2020
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://cronfa.swan.ac.uk/Record/cronfa51023/Download/0051023-12072019155855.pdf
Computational methods for problems pertaining to statistics (62-08) Nonparametric estimation (62G05) Bayesian inference (62F15)
Related Items (8)
A greedy non-intrusive reduced order model for shallow water equations ⋮ Model order reduction method based on (r)POD-ANNs for parameterized time-dependent partial differential equations ⋮ Physics-data combined machine learning for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes ⋮ Surrogate modeling of time-domain electromagnetic wave propagation via dynamic mode decomposition and radial basis function ⋮ A reduced order with data assimilation model: theory and practice ⋮ Reduced-order model-based variational inference with normalizing flows for Bayesian elliptic inverse problems ⋮ Active-learning-driven surrogate modeling for efficient simulation of parametric nonlinear systems ⋮ Parametric non-intrusive model order reduction for flow-fields using unsupervised machine learning
Uses Software
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