Predicting shock-induced cavitation using machine learning: implications for blast-injury models.

ABSTRACT: While cavitation has been suspected as a mechanism of blast-induced traumatic brain injury (bTBI) for a number of years, this phenomenon remains difficult to study due to the current inability to measure cavitation in vivo. Therefore, numerical simulations are often implemented to study cavitation in the brain and surrounding fluids after blast exposure. However, these simulations need to be validated with the results from cavitation experiments. Machine learning algorithms have not generally been applied to study blast injury or biological cavitation models. However, such algorithms have concrete measures for optimization using fewer parameters than those of finite element or fluid dynamics models. Thus, machine learning algorithms are a viable option for predicting cavitation beha

SUBMITTER: Marsh JL 

PROVIDER: S-EPMC10877722 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature