Computational physical modeling is a key resource to complement theoretical and experimental methods in modern scientific research and engineering. While access to large amount of data has favored the use of Artificial Intelligence and Machine Learning (ML) techniques to enhance physical simulations, limitations of purely data-driven methods have emerged as concerns their generalization capability and their intelligibility.
To overcome these limitations, I propose a hybrid approach that originally combines ML methods and equation-based modeling to significantly improve generalization in small-data scenarios, while guaranteeing the intelligibility of the physical models through the use of symbolic representations. To efficiently handle the computational cost associated with the proposed methods, I will implement them in a new software platform that seamlesslyintegrates automated model learning and high-performance simulation.
Thanks to their general-purpose nature, the methods and algorithms developed in this project may be employed in all scientific disciplines and in engineering workflows. In particular, I plan to use them to advance biology and soft robotics by solving challenging modeling tasks.
July 19th
Martedì 19 luglio, ore 16:00-17:00
Biblioteca – Sala Ingegneria Geotecnica (III piano del Chiostro)
ZOOM link: https://uniroma1.zoom.us/j/85146803576