RNDr. MgA. Viktor Hruška, Ph.D.

The topic aims to provide alternatives to classical computational acoustics and optimization methods by applying machine learning and data-driven approaches to challenges in physical acoustics. Methodologically, it will rely on techniques of symbolic regression, equation discovery from data along with their correspondence to analytical modelling based on the underlying laws of physics. This approach offers new possibilities for tackling long-standing issues, such as modeling wave propagation in complex nonlinear media and designing advanced acoustic metamaterials.