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

• Published in: Computational mechanics Vol. 67; no. 6; pp. 1629 - 1643
• Main Authors: Field, Duncan, Ammouche, Yanis, Peña, José-Maria, Jérusalem, Antoine
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021; Springer; Springer Nature B.V
• Subjects: Brain; Calibration; Classical and Continuum Physics; Composite materials; Computational Science and Engineering; Constitutive models; Engineering; Finite element method; Machine learning; Magnetic resonance imaging; Material properties; Mathematical models; Mechanical properties; Microstructure; Original Paper; Parameters; Stress-strain curves; Theoretical and Applied Mechanics; Composites; Constitutive modelling; White matter; Machine learning; FEM

A modular pipeline for improving the constitutive modelling of composite materials is proposed.The method is leveraged here for the development of subject-specific spatially-varying brain white matter mechanical properties. For this application, white matter microstructural information is extracted...