Traumatic brain injuries, often caused by vehicular accidents, are a leading global cause of disability and death. The impact of these accidents imposes sudden inertial forces on the brain, potentially resulting in diffuse axonal injuries (DAIs), a common form of deep brain injury. However, the precise biomechanics underlying DAIs remain poorly understood. Recent observations suggest that shear shock waves in the brain may be a primary cause of DAIs. Brain tissue, characterized by nonlinear and viscous behavior, transforms smooth surface excitations into high-acceleration shear shock waves deep within the brain.
Understanding how these brain tissues behave is of utmost importance for evaluating the risks associated with traumatic impacts. This project is dedicated to developing machine learning models based on artificial neural networks to uncover the governing equations responsible for generating shear shock waves. These models will be constructed using custom numerical simulations and experimental ultrasound data. The ultimate goal is to enhance our insight into brain mechanics and the behavior of soft tissues during traumatic events. Additionally, this research has the potential to refine existing injury assessment criteria and facilitate the development of improved safety equipment.