Scientific Machine Learning for Computational Engineering (SMiLE)

SMiLE builds upon Scientific Machine Learning as an emerging research field focusing on the exploitation of successful machine learning techniques from computer science for the solution of complex problems in physical sciences and engineering. Specifically, SMiLE will contribute in three industrial challenges jointly determined with SEAT, World Sensing and ESI-Group. It is designed as a unique project where all participants (researchers and industrialists) work together and contributions from the different teams are interweaved to exploit synergies and complementarities.
The project will develop novel computational strategies to simulate industrial problems combining data-intensive technology and frontier physics-based models and solvers (including reduced order models). SMiLE has been designed as a unique project where every team works together bringing their own individual expertise in every angle of the research. The three teams will contribute to every task, although only one leader will be in charge of the execution of each task. Each scientific and technological objective in SMiLE has lead researcher from any the three institutions. They are chosen depending on their expertise and assigned tasks independently of their location.
UPC will contribute in every task and it will lead two specific ones setting the foundations for cognitive digital twins. These tasks are: T1.2 Efficient reduced-order solvers in robust and efficient computational engineering solvers and T2.1 Bayesian model updating in model updating, data assimilation and Uncertainty Quantification.
The first one entails the development of novel complexity reduction techniques, both based on model reduction (combined also with multi-fidelity and domain decomposition approaches) and deep learning strategies. By contrast, the second one focusses on developing new Markov-Chain Monte-Carlo accounting for the errors associated with the data, the full-order model and the Machine Learning surrogate.

Grant PID2020-113463RB-C32 funded by