Machine Learning for Fluid Mechanics: Analysis, Modeling, Control and ClosuresMonday 24 February 2020 - Friday 28 February 2020
1) REGISTRATION CLOSED!
2) This lecture will be hosted by the Free University of Brussels (ULB), located on the Erasme Campus, Madeleine De Genst , building W - W.1.302, rue Meylemeersch 20 (entrance 6), 1070 Anderlecht.
Big data and machine learning are driving extensive economic and social changes and are permeating every area of applied science. Face or voice recognition, real-time language translators, self-driving cars, advanced social media, and customer analytics are just some of the products that the data revolution has provided in the last decade. The success of these products relies on the ability of the underlying algorithms to recognize (and classify) the relevant information out of an unwieldy large amount of data and learn from it, by building simple models that enable fast and accurate predictions. Every area of applied science is increasingly benefitting from such powerful tools.
Fluid mechanics is historically a field of big data as experimental and numerical methods provide datasets of ever-growing size and resolution. The ongoing big data revolution, which has its roots in computer science, statistics, pattern recognition, and artificial intelligence fields, is now entering the fluid mechanic community and is extensively improving the way we analyze data and extract knowledge from it.
This new course aims at providing a unified treatment of the machine learning tools that are now paving the way towards advanced methods for model order reduction, system identification, and flow control. The course will gather ideas and notions from various fields, starting from the data decompositions that were pioneered in fluid mechanics and moving towards machine learning methods that were initially developed in machine vision, pattern recognition, and artificial intelligence. This material will be supported with a comprehensive review of the mathematical background and the theory of dynamical systems, including a review on stability analysis for fluid flows and system identification. Furthermore, the lectures will be complemented with a practical exercise and coding sessions that will provide hands-on experience and a reference/starting point to develop a computational proficiency on the subject.
The covered spectra of topics will range from introductory to state of the art research methods, to make the participants capable of exploiting the enormous opportunity offered by the current big data revolution, and able to keep track of the rapid evolution of the field. At the end of the course, the attendees will be capable of designing advanced tools to analyze numerical and experimental data, perform model order reduction, data-driven system identification, and flow control. Whilst the course is intended primarily for the use by fluid dynamics practitioners, it is believed that most of its content will flow through the technological pipeline into a broad spectrum of applications that could include automotive, aeronautical, wind energy, ship designers, and process engineers.
The lecture series will host a poster session, which will allow the participants to further exchange and interact with the lecturers. All the participants are encouraged to submit a 1-page abstract before December 1st, 2019. Please note that the number of participants is limited and admission will be granted on a first come, first served basis.
The Lecture Series codirectors are Miguel A. Mendez from the von Karman Institute (Belgium), Alessandro Parente from the Université libre de Bruxelles (Belgium), Andrea Ianiro from Universidad Carlos III de Madrid (Spain), Bernd R. Noack from Harbin Institute of Technology, Shenzhen and TU Berlin (GERMANY) and Steven L. Brunton from University of Washington (USA).