AI and digital twins in metallurgy – Front-tracking modeling of evolving interface networks
ANNUAL GROSS SALARY: ABOUT 26K€
MINES PARISTECH CEMEF, SOPHIA-ANTIPOLIS (06)
This 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 calculations 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 developments will be validated thanks to pre-existing experimental and numerical data concerning the evolution of grain boundary interfaces during recrystallization and related phenomena for different materials. They will also be integrated in the DIGIMU® software.