I serve as the Principal Investigator for the international Chair HAISLED at ANITI.
Co-chairs: Michael Bauerheim (ISAE-Supaero Toulouse), Paul Novello (IRT Saint-Exupéry Toulouse)

Recently, scientific machine learning (SciML) has expanded the capabilities of traditional numerical approaches, by simplifying computational modeling and providing cost-effective surrogates. However, SciML surrogates suffer from absence of the explicit error control, computationally intensive training phase, and the lack of reliability in practice. HAILSED aims to tackle these challenges by:

• developing novel types of SciML surrogates, the architecture of which incorporates physical and geometric constraints explicitly, and whose validation error can be controlled a posteriori,
• developing novel training algorithms, which leverage domain-decomposition-based approaches and utilize model parallelism,
• hybridizing SciML surrogates with state-of-the-art numerical solution methods, which will be achieved by developing AI-equipped nonlinear field-split and domain-decomposition-based preconditioning strategies.

Successful realization of this project has a potential to pave the way towards efficient and error-controlled solution of large-scale multi-physics and multi-scale problems by combining the efficiency of SciML surrogates with the accuracy and reliability of standard numerical approaches.