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DY: Fachverband Dynamik und Statistische Physik

DY 42: Focus Session: Computing with Dynamical Systems: New Perspectives on Reservoirs and Applications I – Fundamentals

DY 42.1: Invited Talk

Thursday, March 21, 2024, 09:30–10:00, BH-N 243

Is predicting chaos and extreme dynamics possible? An overview of (some) scientific machine learning approaches — •Luca Magri — Imperial College London, London, United Kingdom

The ability of modelling reality to predict the evolution of complex systems is enabled by principles and empirical approaches. Physical principles, for example conservation laws, are extrapolative (until the assumptions upon which they hinge break down): they provide predictions on phenomena that have not been observed. Human beings are excellent at extrapolating knowledge because we are excellent at finding physical principles. On the other hand, empirical modelling provides correlation functions within data, which are useful when principles are difficult to deduce. Artificial intelligence and machine learning are excellent at empirical modelling. In this talk, the complementary capabilities of both approaches will be merged (scientific machine learning). The approaches will achieve real-time modelling and optimization of nonlinear, unsteady and uncertain dynamical systems with chaotic and turbulent dynamics, which exhibit extreme events. The focus of the talk is on computational methodologies for modelling and optimization of chaotic flows with data-driven strategies that involve reservoir computing. I will conclude the talk with some lessons that we have learnt, and a discussion on future directions including quantum reservoir computing.

Keywords: scientific machine learning; reservoir computing; chaos; extreme events; turbulence