Speaker
Description
Partial differential equations (PDEs) are invaluable tools for modeling complex physical phenomena. However, only a limited number of PDEs can be solved analytically, leaving the majority of them requiring computationally expensive numerical approximations. To address this challenge, reduced order models (ROMs) have emerged as a promising field in computational sciences, offering efficient computational tools for real-time simulations, suited to manage digital twins. In recent years, deep learning techniques have played a pivotal role in advancing efficient ROM methods with exceptional generalization capabilities and reduced computational costs, especially for parametric settings and turbulent flows. In this talk we explore how classical ROM techniques can be elevated through the integration of some deep learning models. We will introduce hybrid approaches, which consider both physics-based and purely data-driven techniques, as well as aggregated ones, where the model is built as a combination of different pre-trained models. Examples will deal with parametric flows also with turbulence.
