Helios (High pErformance LearnIng for cOmputational phySics) is a multi-laboratory academic workgroup, coordinated by CERFACS and ISAE-Supaéro.
Helios focuses on investigating recent developments in the field of Machine Learning for their potential to revolutionize computational physics, as they have e.g. the field of image processing. Specifically, Deep Learning, with the training of Artificial Neural Networks, is a very promising technique which is actively investigated for several reasons: its capacity to systematically extract information from previously underexploited databases; its ability to integrate complex multiscale patterns in physical models, to a level of complexity never reached in traditional hand-designed approaches; and for compression, generation and parametrization issues regarding high-dimensional data.
To see some specific examples of the investigated topics, see the research pages.
The involvement of actors throughout the laboratory is key to this initiative. Indeed, this multi-disciplinary topic requires agility to quickly explore topics and seek out opportunities to apply new techniques to existing fields, and select the most promising ones. This means that inside Helios, interns, engineers, Ph.D. students, Post-docs and researchers work together on a daily basis. For a full list of affiliated members, see the team page.
Helios is tightly connected to CERFACS' partners.
This website hosts some databases that are freely available. These datasets are meant to be of relevance for physical modeling purposes, but framed as a learning problem in order to apply machine learning techniques to them. They are usually linked to a publication, available on arXiv, and a repository of code to help explore it, on CERFACS' Gitlab. Please feel free to explore these datasets, and any feedback is welcome.