On-the-Fly Adaptivity for Nonlinear Twoscale Simulations Using Artificial Neural Networks and Reduced Order Modeling

On-the-Fly Adaptivity for Nonlinear Twoscale Simulations Using Artificial Neural Networks and Reduced Order Modeling

A multi-fidelity surrogate model for highly nonlinear multiscale problems is proposed. It is based on the introduction of two different surrogate models and an adaptive on-the-fly switching. The two concurrent surrogates are built incrementally starting from a moderate set of evaluations of the full...

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Bibliographic Details
Main Authors: Felix Fritzen, Mauricio Fernández, Fredrik Larsson
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-05-01
Series:Frontiers in Materials
Subjects:
reduced order modeling (ROM)
machine learning
artificial neural networks (ANN)
surrogate modeling
error control
on-the-fly model adaptivity
Online Access:https://www.frontiersin.org/article/10.3389/fmats.2019.00075/full

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https://www.frontiersin.org/article/10.3389/fmats.2019.00075/full

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