Stage, Solaize, Lyon, France.| Entreprise/Organisme : | IFP Energies Nouvelles | | Niveau d'études : | Master | | Sujet : | Exploring Transfer Learning techniques with Generative AI for the prediction of chemical processes performance | | Date de début : | Mars 2025 | | Durée du contrat : | 5-6 mois | | Secteur d'activité : | Energies Renouvelables | | Description : | IFPEN is an important player in the triple energy, ecological, and digital transition by offering differentiating technological solutions in response to societal and industrial challenges of energy and climate. The implementation of new methodological approaches combining "data science and experimentation" is among the studied solutions that allow for faster progress and reduced R&I costs.
The prediction of the output impurities content, such as sulphur or nitrogen, is a key factor when developing new catalysts or new processes. Data scarcity and poor generalization to new experimental conditions often limit the quality of the kinetic models or even and standard machine learning techniques. One of the solutions for improving models is reusing knowledge from previous datasets. Transfer Learning is a promising approach to model new catalysts or processes. Previous studies conducted at IFPEN led to important improvements using a Bayesian approach. Other techniques, that use Generative Adversarial Networks (GANs), along with feature augmentation, allow model’s deep understanding of the dataset’s feature distribution, thus improving model training and robustness. | | En savoir plus : | NA
TL_GAN_Internship_proposition2024.pdf | | Contact : | youba.abed@ifpen.fr |
|
