Scientific Machine Learning for Computational Engineering

SMiLE project

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 everybody works 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). Each group, will bring their own individual expertise in every angle of the research. But the project has been designed as a unique project where every team works together. Thus, 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.

The project leaded by UniZar, Physics-informed model discovery and learning, deals with the design of novel scientific machine learning techniques whose main ingredient is the leverage of known physical laws for the phenomenon at hand.


Elías Cueto (PI)

David González 

Icíar Alfaro

Alberto Badías

Beatriz Moya


Downloadable preprints can be found here.



Grant PID2020-113463RB-C31 funded by MCIN/AEI/ 10.13039/501100011033