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

Abstract:

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 from diffusion magnetic resonance imaging (dMRI) scans, and used to generate hundreds of representative volume elements (RVEs) with randomly distributed fibre properties. By automatically running f...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/s00466-021-02009-1

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Role:
Author
Publisher:
Springer Nature Publisher's website
Journal:
Computational Mechanics Journal website
Volume:
67
Issue:
2021
Pages:
1629–1643
Publication date:
2021-04-28
Acceptance date:
2021-03-20
DOI:
EISSN:
1432-0924
ISSN:
0178-7675
Language:
English
Keywords:
Pubs id:
1168863
Local pid:
pubs:1168863
Deposit date:
2021-03-22

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