Projet REMODEL (Horizon Europe, appel à projets 2022)
Action : Staff Exchanges
Panel : Mathematics
Statut du projet : SIGNED

The subject of this proposal is “mathematical aspects of deep learning algorithms and their applications”. We will address several questions related to the mathematical foundations of neural networks and set up an interdisciplinary team to aidthe design of test problems and validate the research results obtained. The impact of neural networks and deep learning in recent years has been profound and unprecedented. But in the wake of the vast progress in this area, several questions and concerns have been raised about the robustness, reliability, accuracy, reproducibility and feasibility of neural networks. It is widely recognised that the mathematical sciences, are a key enabling technology in many aspects of machine learning, not the least to resolve some of the above mentioned concerns. Mathematical language and formalism can bring morerigour and precision to the understanding of the deep learning methodology. Recently, deep learning methods have been applied to physical simulations, and to discover the underlying mathematical model. Most of the work in this area has been limited to proof-of-concept and has not been applied to practical problems. An alternative approach is to make use of reduced order modelling, and this can also be combined with machine learning methods. The aim of this project is to understand, study, prove, and test the properties of deep learning algorithms using ideas from dynamical systems, geometry and optimisation. The research objectives are three-fold. The first pertains to understandingthe general properties of neural networks and their impact on a range of applications. The second is about the use of neural networks for investigating dynamical systems, and their applications to physical models. Finally we establish a new and complementary network of mathematicians from European and third countries for studying neural networks and the methods of deep learning with connections to a range of application areas through staff exchanges.

Mots clés : deep learning, neural networks, numerical analysis, hyperspectral image analysis, inverse problems, equivariance, computer vision, model order reduction, dynamics discovery, accelerated simulations

Participants :
- Université norvégienne de sciences et de technologie (Norvège) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 349600.0 €
- Université Emory (Etats-Unis) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
- Université de Bath (Royaume-Uni) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
- Université de Kōbe (Japon) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
- Université Simon Fraser (Canada) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
- Université de technologie d'Eindhoven (Pays-Bas) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 124200.0 €
- Université de Cambridge (Royaume-Uni) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €