PhD opportunities

AI and digital twins in metallurgy – Front-tracking modeling of evolving interface networks

Thesis proposal

Area of expertiseMathématiques numériques, Calcul intensif et Données
Doctoral SchoolDoctoral School for Fundamental and Applied Sciences
SupervisorM. Marc BERNACKI
Research unitCentre for material forming
Starting dateOctober 1st 2022
KeywordsDigital twins, AI, Computational Metallurgy, Interface networks, Front tracking, To Real Motion algorithms
AbstractThis PhD will be dedicated to the use of different deep neural network (DNN) strategies for different applications. First, a supervised neural network-based remeshing strategy will be developed to improve the computational cost and efficiency of numerous remeshing operations used in the Lagrangian ToRe- alMotion method. Secondly, supervised deep neural network strategies and deep reinforcement learning strategies will be trained on a large numerical database built in the project thanks to the new efficient ToRealMotion calculation capabilities and also enriched with experimental data already available among the partners. Thanks to this, the acceleration of R&D calcula- tions by coupling mesoscopic computations with automatically proposed mesoscopic results coming from the trained DNN will be investigated. Moreover, automatic interpretation of some microstructural singularities will also be tested. The develop- ments will be validated thanks to pre-existing experimental and numerical data concerning the evolution of grain boundary inter- faces during recrystallization and related phenomena for different materials. They will also be integrated in the DIGIMU® software.
ProfileDegree: MSc or MTech in Applied Mathematics, with excellent academic record.
Skills: Numerical Modeling, programming, proficiency in English, ability to work within a multi-disciplinary team.
FundingAutre type de financement