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Jose del Aquila "Exploring Complex Problems in Fluid Dynamics: from CFD o Experiments Leveraging ML"

10:00 am
Friday, February 16, 2024
270 NCB Hall
Faculty Host:  Dr. Rakesh Kapania

Abstract:  In the quest to conquer the vast complexities of both air and sea, this seminar looks into the revolutionary integration of Scientific Machine Learning within aerospace and ocean engineering. This seminar will be a journey along applications of some of the lastest scientific machine learning algorithms to engineering problems. During this journey, I will showcase the transformative role of Machine Learning (ML) in several areas: optimizing marine vessel efficiency, controlling flow with innovative designs, and accurately predicting ship motions under challenging conditions, among other notable applications. By bridging the gap between theoretical innovation and practical application, we showcase ML’s potential to redefine the paradigms of design and safety in engineering. Join to explore how ML not only advances our scientific understanding but also paves the way for sustainable, adaptable engineering solutions, setting a new course for the future of aerospace and ocean systems.

Bio:  José del Águila Ferrandis is a Ph.D. candidate at MIT, specializing in the application of Scientific Machine Learning to address practical engineering challenges within fluid dynamics and structural analysis. His innovative research, focusing on integrating physical laws with ML to predict complex phenomena, has significant implications for aerospace and ocean engineering. José’s collaborations with industry leaders like NVIDIA and Chevron, along with his work at the MIT Sea Grant–Design Lab, exemplify his commitment to leveraging computational advances for real-world solutions. His academic excellence is recognized by prestigious awards, including "la Caixa" and Fulbright scholarships, which were given for his potential to transform engineering practices through scientific machine learning.