Using dropout based active learning and surrogate models in the inverse viscoelastic parameter identification of human brain tissue

Using dropout based active learning and surrogate models in the inverse viscoelastic parameter identification of human brain tissue

Inverse mechanical parameter identification enables the characterization of ultrasoft materials, for which it is difficult to achieve homogeneous deformation states. However, this usually involves high computational costs that are mainly determined by the complexity of the forward model. While simul...

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Bibliographic Details
Main Authors: Jan Hinrichsen, Carl Ferlay, Nina Reiter, Silvia Budday
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-01-01
Series:Frontiers in Physiology
Subjects:
active learning
neural network
surrogate model
parameter identification
human brain tissue
Online Access:https://www.frontiersin.org/articles/10.3389/fphys.2024.1321298/full

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