In this presentation, we review our recent work in using the combination of instrumented mouthguards and finite element modeling to correlate to cognitive changes in American football players. We employ custom fit mouthguards to compute obtain kinematics of the skull and then use the…
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,…
Multi-principal metal alloys (MPEAs) are an active research area for their desirable mechanical performance and electrochemical resistance. However, design of new MPEAs is complex. The potential composition space for MPEAs is massive and Edisonian approaches are slow. Artificial intelligence (AI) guided discovery is promising…
Machine learning approaches to materials discovery have great potential, but currently face some limitations in data availability, curation, and potential biases. One promising approach is using generative machine learning models to produce new data points representing novel material compositions and structures. This can help…