SHyNE (ESR 2022):

2022-2025 (under project approval): Project Architectural Design of Microstructures less Sensitive to Hydrogen using NEural Networks (SHyNE) aims to collabrate three laboratories spans of material science and computer science to develop new material being more resistant to corrosion of Hydrogen. The deep learning is introduced to model the new distributions of metallurgical defects. This project helps to realize the true hydrogen economy using hydrogen as a clean and mobile energy carrier and fuel source.

• Collabration labotories: Institut Pprime, LaSIE , L3i
• with Jamaa Bouhattate, FEAUGAS Xavier Gilbert Henaff, OUDRISS Abdelali, Antoine Falaize, GELICUS Antony, BERZIOU Cyril, COHENDOZ Stéphane, LOTTE Guillaume, HENAFF Gilbert, Denis Bertheau, Guillaume.benoit, Jean-Christophe Burie