Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning

A book based on the von Karman Institute Lecture Series
Machine Learning for Fluid Mechanics: Analysis, Modeling, Control and Closures

About the book

The book entitled "Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning" will be edited by Cambridge University Press and is the result of a lecture series organized by the von Karman Institute for Fluid Dynamics and the Université Libre de Bruxelles.
This is the first textbook on Machine Learning for key fluid mechanics problems including analysis, modeling, control and closures. The Machine learning revolution is driving extensive economic and social transformations and its potential is now being discovered by the community of fluid mechanics researchers. The unprecedented amount of high-quality data provided by ever increasing experimental and numerical capabilities is driving this revolution. Machine Learning extracts knowledge from data without hinging on first principles and proposes a new paradigm to the applied scientist: reverse the natural flow of the scientific method and use data to discover, rather than validate, new hypotheses and models.
This new paradigm enlarges the available toolboxes of fluid dynamicists but challenges undergraduate students and practitioners alike in gathering the required knowhow from all the disciplines that are intersected by its methods. The purpose of this book is to provide a unified and pedagogical treatment of the machine learning tools that are now paving the way towards advanced methods for model order reduction, system identification, flow control, and data-driven turbulence models. Fluid Mechanics poses challenges and opportunities for machine learning tools by powerful first principles which can complement data-driven theories.
The proposed textbook is designed to complement undergraduate classes on datadriven science, applied mathematics, scientific computing, and fluid mechanics, as well as to serve as a reference for engineers and scientists working in these fields.

Why is a book needed?

Fluid Mechanics is historically a field of Big Data and hence offers a fertile ground to the data-driven paradigm, which has already provided insights in grand challenges such as turbulence control or turbulence modeling. As Machine Learning continuously augments or replaces more traditional methods, the demand for dedicated courses on the topic and qualified people has exponential grown in industry and academia alike. In aerospace and mechanical engineering programs, more and more prestigious universities are creating new chairs and opening new positions on machine learning for fluid dynamics.