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Hands on Machine Learning for Fluid Dynamics 2024

Date: May 27, 2024 - May 31, 2024
Location: von Karman Insitute on-site / online, , ,
Contact: secretariat@vki.ac.be
Phone:

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Motivation

Big data and machine learning are driving comprehensive economic and social transformations and are rapidly re-shaping the toolbox and the methodologies of applied scientists. Machine learning tools are designed to learn functions from data with little to no need for prior knowledge. As continuous developments in experimental and numerical methods improve our ability to collect high-quality data, machine learning tools become increasingly viable and promising in disciplines rooted in physical principles. Fluid dynamics is one of them.

Objectives

This course gives an overview and practical hands-on experience in integrating machine learning in fluid dynamics. The course originated as a compressed version of the course Machine Learning for Fluid Dynamics, given at the Research Master program at the von Karman Institute. After a brief review of the machine learning landscape, we show how to frame problems in fluid mechanics as machine learning problems, and we explore challenges and opportunities. Attendees will be guided through a series of tutorial sessions in Python and will tackle several relevant applications: aeroacoustics' noise prediction, turbulence modelling, reduced-order modelling and forecasting, meshless integration of (partial) differential equations, super-resolution and flow control.

Approach

All lectures consist of a short theoretical session and a set of practical exercises using Python. Moreover, several group exercises will be provided. These are working sessions in which the role of the instructor is marginal, and participants should be able to complete their assignment independently. These will provide hands on experience and consolidate the understanding of the provided tools.

Pre-requisites

The course pre-requisite is a basic understanding of Python programming and basic knowledge of Calculus, Linear Algebra and Fluid dynamics. The course is pitched for undergraduate and graduate students alike, as well as practitioners in the fields.

Certificates

1. Certificates of Participation. This will certify that the participant attended all lectures. No grading is involved.

2. Graded Course Certificate. This will be a graded certificate, which participants can use to obtain ECTS in their home Universities. The grading is based on the results of an online exam, which will be organized within one month after the course. The online exam will include both theoretical questions and exercises. The evaluation criteria for the activities in Python are based on the clear and bug-free implementation of the various algorithms developed during the course. The final grade will be the average between the quiz results and the exercises. The estimated workload of the course consists of 22.5 h of lectures and 22.5 h of self-study (of which 7.5 during the course).

 

A discount of 50% is applied for an online participation

Registration deadline for on site participation: 13/05/2024

Registration deadline for online participation: 20/05/2024

Monday 27 May 2024: Introduction and Fundamental of Optimization

08:00 Registration

08:45 Welcome Address and Course Introduction

09:00 Lecture 1: What is Machine Learning?
Miguel A. Mendez, von Karman Institute

10:30 Coffee Break

11:00 Lecture 2: Exercises on Regression and Uncertainty Quantification
Miguel A. Mendez, von Karman Institute

12:30 Lunch Break

14:00 Lecture 3: A Review of Optimization Tools
Pedro Marques, von Karman Institute

15:30 Coffee Break

16:00 Lecture 4: Bio-Inspired Optimization: Genetic Algorithms and Particle Swarms
Miguel Mendez, von Karman Institute

17:30 End of day

Tuesday 28 May 2024: Regression Methods from Machine Learning

09:00 Lecture 5: Regularized and Constrained Radial Basis Functions
Manuel Ratz, von Karman Institute

10:30 Coffee Break

11:00 Lecture 6: The Bayesian Formalism and Kernel Methods
Miguel Mendez, von Karman Institute

12:30 Lunch Break

14:00 Lecture 7: Gaussian Processes and Bayesian Optimization
Miguel Mendez, von Karman Institute

15:30 Coffee Break

16:00 Lecture 8: Artificial Neural Networks and Deep Learning
Mr. Jan Van den Berghe, von Karman Institute

17:30 End of day

Wednesday 29 May 2024: Regression Methods for Dynamical Systems

09:00 Lecture 9: Deep Learning for Turbulence Modeling, Part I
Matilde Fiore, von Karman Institute

10:30 Coffee Break

11:00 Lecture 10: Exercises on machine learning for Thermodynamic Modelling
Samuel Ahizi, von Karman Institute

12:30 Lunch Break

14:00 Lecture 11: Data Assimilation and System Identification 2.0: Digital Twins
Miguel Mendez, von Karman Institute

15:30 Coffee Break

16:00 Lecture 12: Neural ODEs and Dynamical Systems
Samuel Ahizi, von Karman Institute

17:30 End of day

Thursday 30 May 2024: Dimensionality Reduction

09:00 Lecture 13: Linear Autoencoders and Data Driven Modal Analysis
Miguel A. Mendez, von Karman Institute

10:30 Coffee Break

11:00 Lecture 14: Manifold Learning and Nonlinear methods
Miguel A. Mendez, von Karman Institute

12:30 Lunch Break

14:00 Lecture 15: An Introduction to Variational Autoencoders
Joachim Dominique, Cenaero

15:30 Coffee Break

16:00 Lecture 16: Exercices on Dimensionality Reduction
Pedro Marques and J. van den Berghe, von Karman Institute

17:30 End of day

Friday 31 May 2024: Flow Control and Digital Twinning

09:00 Lecture 17: Fundamentals of Optimal Control Theory and Reinforcement Learning
Miguel A. Mendez, von Karman Institute

10:30 Coffee Break

11:00 Lecture 18: Value-based vs Policy-based Reinforcement Learning
Lorenzo Schena, von Karman Institute

12:30 Lunch Break

14:00 Lecture 19: The Actor-Critic Formalism in Reinforcement Learning
Romain Poletti, von Karman Institute

15:30 Coffee Break

16:00 Lecture 20: Reinforcement Twinning: From Digital Twins to Model-Based Reinforcement Learning
Miguel A. Mendez, von Karman Institute

17:30 End of day

Check out our eligibility criteria

Special reduction

Rebates can be given for group subscriptions along the following scheme :

- 5 persons of the same organization -2%
- 10 persons of the same organization -5%
- 20 persons of the same organization -10%


Sales Conditions:

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  • on-site - Undergraduate Students (Proof Needed) (Full) 350.0
    on-site - PhD Students (Proof Needed) (Full) 960.0
    on-site - Recognized Universities / Research Center (Full) 1520.0
    on-site - Commercial Organizations (Full) 1920.0
    on-site - Special Registration (Full) 0.0
    on-site - Lecturer (Full) 0.0
    online - Undergraduate Students (Proof Needed) (Full) 175.0
    online - PhD Students (Proof Needed) (Full) 480.0
    online - Recognized Universities / Research Center (Full) 760.0
    online - Commercial Organizations (Full) 960.0
    online - Special Registration (Full) 0.0
    online - Lecturer (Full) 0.0

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    Hands on Machine Learning for Fluid Dynamics 2024


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