Machine Learning Constitutive Response of Multiscale Fibrous Materials

Hi-fidelity multiscale simulations using frameworks such as MuMFiM are necessary to develop an understanding of fibrous materials on an engineering or biological scale. However, they require significant computational resources and HPC expertise that are out of reach for most biomechanicians. This project seeks to develop constitutive models for fibrous materials making use of machine learning methodologies.

This has led to the development of a framework for using neural-network hyperelastic materials in MuMFiM and a set of physics constrained neural-networks that can estimate the constitutive response of fibrous materials.

In an example test case, a model of a FCL that took 432 GPU-hours on 72-NVIDIA V100 GPUs could be solved in less than 30 minutes on a m1-macbook pro laptop. By continuing to incorporate the complex fiber network physics into these machine learned models, more of the biomechanics community can access the benefits of high-fidelity multiscale simulations.