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

Lectures and Videos

Part I: Coherent Structures

Opening speech

Lecture 1 - Analysis, Modeling and Control of the Cylinder Wake

Prof. Bernd R. Noack, Harbin Institute of Technology, Shenzhen, China and TU Berlin, Germany

Lecture 2 - Coherent Structures in Turbulent Flows

Prof. Javier Jiménez, Universidad Politecnica de Madrid, Spain

Lecture 3 - The Proper Orthogonal Decomposition

Prof. Scott T.M. Dawson, Illinois Institute of Technology, USA

Lecture 4 - The Dynamic Mode Decomposition: From Koopman Theory to Applications

Prof. Peter J. Schmid, Imperial College London, UK

Part II: Mathematical Analysis

Lecture 5 - Mathematical Tools, Part I: Continuous and Discrete LTI Systems

Prof. Miguel A. Mendez, von Karman Institute for Fluid Dynamics, Belgium

Lecture 6 - Mathematical Tools, Part II: Time-Frequency Analysis

Prof. Stefano Discetti, Universidad Carlos III de Madrid, Spain

Lecture 7- Generalized and Multiscale Data-Driven Modal Analysis

Prof. Miguel A. Mendez, von Karman Institute for Fluid Dynamics, Belgium

Lecture 8 - Applications and Good Practice

Prof. Andrea Ianiro, Universidad Carlos III de Madrid, Spain

Part III: Dynamical Systems

Lecture 9 - Modern Tools for the Stability Analysis of Fluid Flows

Prof. P.J. Schmid, Imperial College London, UK

Lecture 10 - Linear Dynamical Systems and Control

Prof. Scott T.M. Dawson, Illinois Institute of Technology, USA

Lecture 11 - Nonlinear Dynamical Systems

Prof. Steve L. Brunton, University of Washington, USA

Lecture 12 - Methods for System Identification

Prof. Steve L. Brunton, University of Washington, USA

Part IV: Reduced Order Modeling

Lecture 13 - Introduction to Machine Learning Methods

Prof. Steve L. Brunton, University of Washington, USA

Lecture 14 - Machine Learning in Fluids: Pairing Methods with Problems

Prof. Steve L. Brunton, University of Washington, USA

Lecture 15 - Machine Learning for Reduced-Order Modeling

Prof. Bernd R. Noack, Harbin Institute of Technology, Shenzhen, China and TU Berlin, Germany

Lecture 16 - Advancing Reacting Flow Simulations with Data-Driven Models: Chemistry Accelerations and Reduced-Order Modelling

Prof. Alessandro Parente, Université Libre de Bruxelles, Belgium

Part V: Control, Closures and Perspectives

Lecture 17 - Reduced-Order Modeling for Aerodynamic Applications and MDO

Dr. Stefan Görtz, German Aerospace Center (DLR), Germany

Lecture 18 - Machine Learning for Turbulence Control

Prof. Bernd R. Noack, Harbin Institute of Technology, Shenzhen, China and TU Berlin, Germany

Lecture 19 - The Computer as Turbulence Researcher

Prof. Javier Jiménez, Universidad Politecnica de Madrid, Spain

Round Table