Objectives of the chair

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 chair aims to tackle these challenges by

(1) 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.

Principal investigator

(2) Developing novel training algorithms, which leverage multilevel and domain-decomposition-based approaches and utilize data and model parallelism.

(3) 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.

Co-chair

By successfully carrying out the proposed methodologies, HAISLED chair aims to unlock efficient and error-controlled solutions for large-scale multiphysics and multiscale problems through the synergy of SciML surrogate models and classical numerical approaches.

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