Abstracts

In this presentation we look at various finite element methods for modeling biological matter. Because biological matter is nearly incompressible, volumetric locking must be accounted for. Mixed finite elements are known to overcome locking and in addition are particularly suited for problems where a…

Self-assembling polymers have become an important component of armor materials and protective coatings due to their ability to absorb and dissipate shock energy and rapidly self-seal punctures caused by projectiles. Optimal self-sealing polymers must be both elastically resilient and have fast molecular diffusion during…

Blunt thoracic trauma is known to result in a variety of injuries, from contusion to edema. In prior we work, we have shown that the presence of the heterogeneous bronchi structures within lungs alter the strain fields within the lung, making deeper inter-bronchial tissues…

An important consideration for space missions is the likelihood of contamination, either of terrestrial organisms to a planetary body which may sustain life (forward contamination), or of potential extraterrestrial lifeforms that are brought back through sample return (backward contamination). To identify the limits of…

A continuum theory is formulated for large deformations, thermal effects, constituent interactions, and degradation of soft biological tissues. Such tissues consist of one or more solid and fluid phases and can demonstrate nonlinear anisotropic elastic, viscoelastic, thermoelastic, and poroelastic deformation mechanisms. Under extremely large…

This study presents a computational analysis of the spinal health challenges faced by fighter pilots subjected to repeated high gravitational forces in training scenarios. Previous studies have indicated a heightened risk of acute spinal injuries and accelerated disc degeneration, particularly in the cervical spine…

High-fidelity computational simulations and physical experiments of hypersonic flows are resource intensive. Training scientific machine learning (SciML) models on limited high-fidelity data offers one approach to rapidly predict behaviors for situations that have not been seen before. However, high-fidelity data is itself in limited…

We present a machine learning framework, leveraging several tools from Materials Informatics and microstructure-explicit simulations, to rapidly explore these high-dimensional spaces and establish structure-property relationships for material systems. The framework allows for a rapid establishment of a learned structure-property relationship for enabling materials design…

The development of Machine Learning Interatomic Potentials (MLIPs) has gained significant traction in recent years. These new data-driven potential energy approximations often lack the physics-based foundations that inform many traditionally-developed interatomic potentials and hence require robust validation methods for their applicability, accuracy, computational efficiency,…