Mardi 24 septembre 2024 à 11h00 / Amphithéâtre François Canac, LMA
Abstract : Scientific Machine Learning (SciML) has emerged as a discipline combining Machine Learning, Data-Science and Computational Methods leading to new powerful tools in the realm of Computational Simulation.
Here, I briefly describe our ower experience over the last ten years in combining Computational simulation, High-Performance computing, and Machine Learning (the core components of Scientific Machine Learning) to provide solutions in diGerent application fields. We particularly emphasize applications within the realm of Energy Transition, like, for instance, Seismic, CCS, and Wind Energy. I go into more technical detail in two specific topics: Uncertainty Quantification in Carbone Capture and Sequestration computational modeling and (if time allows) Full Wave Inversion with Quantified Uncertainty.
Fernando Rochinha - Federal University of Rio de Janeiro / Departamento de Engenharia Mecânica