Next Talk  - on 27 March 2024 15:00 CET

 

Prof. Miguel Alfonso Mendez
Associate Professor at the von Karman Institute for Fluid Dynamics

 

 Trends and Challenges in Scientific Machine Learning for Engineering

 

Abstract: The use of machine learning in engineering is continuously growing and evolving. Machine learning offers robust tools to identify patterns and learn functions from large datasets, and the ever-growing availability of experimental and numerical data makes it viable also for disciplines traditionally rooted in physical principles, mechanistic modelling and differential equations such as fluid dynamics. Blending machine learning and physical modelling is the essence of scientific machine learning, an emerging field with promising avenues in engineering and applied sciences. This talk overviews how physical priors can be embedded into machine learning models and discusses some major trends and perspectives in inverse modelling and control of engineering systems.

 

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