In the last few years machine learning has made amazing strides in virtually instantaneous predictions for language modelling and image generation. Many scientific and engineering disciplines, such as fluid dynamics, are very computationally expensive to simulate, leading to the natural goal of using machine learning tools to accelerate these predictions. But can tools developed for qualitative predictions with millions of examples be used effectively on quantitative engineering problems with sparse data? Can physics be incorporated into large ML predictions without slowing down the model or hindering its generalization? This seminar will cover some of my group’s efforts towards machine-learning-accelerated flow solvers, turbulent flow predictions, and maneuvering optimization.

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Please fill in the registration form to recieve the link for joining online.

Location for joining in-person:

Building 29 - ECHO , Hall F, Van Mourik Broekmanweg 5, 2628 XE Delft