Multi-disciplinary Ship Design by Reduced Order Models and Machine Learning

In the framework of the numerical analysis for parametric partial differential equations (PDEs), reduced-order modeling for fluid-structure interaction problems and probabilistic multi-disciplinary ship design, we want to go beyond the state of the art through this strategic collaboration between SISSA mathLab (modelling and scientific computing), Prof. Gianluigi Rozza’s group, and MIT Sea Grant - Department of Mechanical Engineering, Prof. Michael Triantafyllou’s group.
During this collaboration, we aim to develop a multi-disciplinary design framework capable of providing online probabilistic predictions of systems performance and characteristics, based solely on data generated by numerical solvers running off-line.

PROJECT PERIOD: MIT FVG 2 - 2019-2020, further extended to 31/3/2021 because of the COVID-19 pandemic.

PARTNER: MIT, Friuli-Venezia Giulia Region and Scuola Internazionale Superiore di Studi Avanzati - SISSA

From SISSA: Marco Tezzele, Nicola Demo, Gianluigi Rozza (P.I. at SISSA)
From MIT: Luca Bonfiglio, George Karniadakis, Michael Triantafyllou (P.I. at MIT)

 For researchers and SISSA staff | Scuola Internazionale Superiore di Studi  Avanzati