Data-driven Modeling of the Mechanical Behavior of Anisotropic Soft Biological Tissue.

ABSTRACT: Closed-form constitutive models are the standard to describe soft tissue mechanical behavior. However, inherent pitfalls of an explicit functional form include poor fits to the data, non-uniqueness of fit, and sensitivity to parameters. Here we design deep neural networks (DNN) that satisfy desirable physics constraints in order to replace expert models of tissue mechanics. To guarantee stress-objectivity, the DNN takes strain (pseudo)-invariants as inputs, and outputs the strain energy and its derivatives. Polyconvexity of strain energy is enforced through the loss function. Direct prediction of both energy and derivative functions enables the computation of the elasticity tensor needed for a finite element implementation. We showcase the DNN ability to learn the anisotropic mechanical be

SUBMITTER: Tac V 

PROVIDER: S-EPMC10686525 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature