A bi-monthly (online) seminar series discussing applications of Physics-enhanced Machine Learning methods in Engineering practice.
Machine learning (ML) applications in engineering have been applied to both small and large-scale problems. However, the necessity of using large training datasets together with the ‘black box’ nature of ML learning methods may compromise their interpretability, which is necessary for practical applications. In this context, Physics-informed machine learning (Φ-ML) is an emerging subfield to tackle such difficulties. Φ-ML methods aim to integrate known physical understanding of a phenomenon, e.g. expressed as ODE/PDE/SDEs, into the ML-learning framework. This seminar series plans to explore real-world applications of Φ-ML methods to the Engineering practice.
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Davor Dundovic - The Alan Turing Institute [email@example.com] **
Zack Xuereb Conti - The Alan Turing Institute [firstname.lastname@example.org]
Andrea Pizzoferrato - The Alan Turing Institute / University of Bath [email@example.com]
David Massegur, Themistoklis Botsas.
Recordings of past seminars can be found here.