Data-driven Tissue Mechanics with Polyconvex Neural Ordinary Differential Equations.

ABSTRACT: Data-driven methods are becoming an essential part of computational mechanics due to their advantages over traditional material modeling. Deep neural networks are able to learn complex material response without the constraints of closed-form models. However, data-driven approaches do not a priori satisfy physics-based mathematical requirements such as polyconvexity, a condition needed for the existence of minimizers for boundary value problems in elasticity. In this study, we use a recent class of neural networks, neural ordinary differential equations (N-ODEs), to develop data-driven material models that automatically satisfy polyconvexity of the strain energy. We take advantage of the properties of ordinary differential equations to create monotonic functions that approximate the derivat

SUBMITTER: Tac V 

PROVIDER: S-EPMC10691864 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature