Lunedì 30 Giugno si terrà un seminario presso l’aula Fassò (Edificio 4a) dalle ore 11:00 dal titolo “Data-Driven Modeling of Complex Fluid Flows via Scientific Machine Learning”.

Il seminario sarà tenuto da Cássio Machiaveli Oishi, São Paulo State University UNESP.

Abstract

Recent advances in machine learning have significantly influenced the simulation of Newtonian fluids, and there is growing interest in extending these techniques to complex fluids with non-Newtonian properties, such as viscoelastic flows. In this talk, we present data-driven frameworks for constructing interpretable reduced-order models of viscoelastic fluids, combining both linear and nonlinear dimensionality reduction methods with the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm. In particular, we explore the development of physics-informed metrics to enhance nonlinear dimensionality reduction tailored to viscoelastic behavior. We also highlight recent progress in the SciML domain, with a focus on applications to droplet dynamics.