Description
Date depot: 16 mai 2019
Titre: Machine Learning for Efficient Numerical Simulations
Directeur de thèse:
Patrick GALLINARI (ISIR (EDITE))
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Intelligence artificielle
Resumé:
Numerical simulation is an essential tool in order to model complex physical processes. Modern parallel codes implement complex algorithms that solve large systems of non-linear or partial differential equations. These computations produce a large amount of data that is usually discarded for future similar computations. The aim of this thesis is to leverage these data using machine learning in order to improve numerical simulation performance.
Doctorant.e: Qu Jingang