Projet AIPHY (Horizon Europe, appel à projets 2023)
Action : Doctoral Networks
Panel : Information Science and Engineering
Statut du projet : SIGNED

AI is a disruptive technology that is currently changing not only many research fields but also substantially challenges current business concepts. Keeping up with the ever-faster progress on one hand side and the ever increasing competition from US and Chinese IT giants is a challenge for European Industry and Research Institutions. Yet, new concepts are required to keep pace. This concept is to intertwine modern AI concepts with strategies from the application field, a strategy where we claim that we can contribute substantially to a novel and promising research field. Particle physics is a formidable basis for such developments as there are no critical, personal data, data can be easily generated; and we have a good understanding of the underlying models and the ground truth. Yet, particle physics has challenges such as detailed questions of new physics and extremely high precision of the results. We identified three main fields where this project will contribute by solving 1) highly ill-posed inverse problems using physics models, 2) handling uncertainties, rare events and give reliable error bounds, and finally, 3) to be able to give explanations of the machine learning results in terms of physics ontologies and models. This highly challenging research agenda is tackled by 9 internationally recognized and leading researchers from both physics and computer science, from 5 universities in 5 European countries, all being member of the 4EU+ Alliance of European Universities. Over 4EU+ an excellent infrastructure for unique training opportunities is available to enable the research fellows to reach the ambitious goals, on one hand side and to educate them in critical and innovative thinking, management and social skills to prepare them optimally for leading positions in academics and industry.

Mots clés : machine learning, inverse problems, physics informed neural networks, uncertainty quantification, explainable ai

Participants :
- Halfspace.io aps (Danemark) — Partner, associated partner, Organismes privés (hors enseignement), subvention : 0.0 €
- Inova+ - innovation services, sa (Portugal) — Partner, associated partner, Organismes privés (hors enseignement), subvention : 0.0 €
- Université de Genève (Suisse) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
- Proxivision gmbh (Allemagne) — Partner, associated partner, Organismes privés (hors enseignement), subvention : 0.0 €
- Facebook ireland limited (Irlande) — Partner, associated partner, Organismes privés (hors enseignement), subvention : 0.0 €
- Université de Milan (Italie) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 518875.2 €
- Centre national de la recherche scientifique (France) — Partner, beneficiary, Recherche, subvention : 282693.6 €
- Sorbonne Université (France) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 282693.6 €
- Université de Copenhague (Danemark) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 301788.0 €
- Université de Heidelberg (Allemagne) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 521078.4 €