Machine learning based multiscale calibration of mesoscopic constitutive models for composite materials: application to brain white matter

DOI10.1007/s00466-021-02009-1zbMath1467.74074OpenAlexW3157533411WikidataQ113326720 ScholiaQ113326720MaRDI QIDQ2037488

Antoine Jérusalem, José-Maria Peña, Yanis Ammouche, Duncan Field

Publication date: 8 July 2021

Published in: Computational Mechanics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00466-021-02009-1

zbMATH Keywords

finite element methodrepresentative volume elementmagnetic resonance imagingmachine learningcomputational homogenizationbrain white matter modeling

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Biomedical imaging and signal processing (92C55) Finite element methods applied to problems in solid mechanics (74S05) Effective constitutive equations in solid mechanics (74Q15) Composite and mixture properties (74E30) Biomechanical solid mechanics (74L15)

Related Items (1)

\(\mathrm{FE^{ANN}}\): an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining

Uses Software

• AdaBoost.MH

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