von Karman Institute Lecture Series and Events

Hands on Machine Learning for Fluid Dynamics

Monday 07 February 2022 - Friday 11 February 2022

VKI secretariat, This email address is being protected from spambots. You need JavaScript enabled to view it.; Phone: +32 2 359 96 04

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 on how to integrate machine learning in fluid dynamics. The course originate as a compressed version of the course Data Driven Fluid Mechanics and Machine Learning, 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: aeroacoustic 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. At the end of each day, the participants will be challenged with a group exercise. These will be working sessions in which the role of the instructor will be marginal: participants must be able to complete their assignment independently to get the course certificate. The course includes a short overview of Python programming and the use of the python packages that will be implemented.

Pre-requisites

Therefore, the only course pre-requisite is a basic understanding of any programming language 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 the exercises and the results of an online quiz, which will be active for 1 hour on Thursday 17th . The students will have the possibility to resubmit their solution to the exercises on Monday 14th . The grades will be sent by Friday 25th . The evaluation criteria for the exercises in Python is based on the clear and bug-free implementation of the various algorithms developed during the course. The quiz will include both theoretical questions and exercises. Examples of questions will be provided during the course. The final grade will be the average between the results of the quiz and the exercises. The estimated workload of the course consist of 22.5 h of lectures, and 22.5 h of self study (of which 7.5 during the course). 

 

Location

Online and on-site

von Karman Institute for Fluid Dynamics

Waterloosesteenweg 72

B-1640 Sint-Genesius-Rode (near Brussels)

Parking and Safety Information

Parking places are available on the premises, just before the security fence.

To enter, please ring the bell at the fence.

It is mandatory to wear the face mask indoors. In the restaurant, you can remove your face mask while eating and drinking.

Programme (CET)

A "questions and answers" session of 15 minutes is organized after each lectures.

Monday 7 February 2022: Introduction and Python Fundamentals

08:00 Registration

08:45 Welcome Address and Course Introduction

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

10:30 Coffee Break

11:00 Lec 2: An Introduction to Python Programming
Mr. Julien Christophe, von Karman Institute

12:30 Lunch Break

13:30 Lec 3: An Introduction to Scientific Computing with Python
Mr. Federico Torres, von Karman Institute

15:00 Coffee Break

15:30 Exercise 1: Regression with Model Selection and Uncertainty Quantification
Prof. Miguel Mendez, von Karman Institute

17:00 End of day

Tuesday 8 February 2022: Optimization Methods for Machine Learning

09:00 Lec 5: A Review of Optimization Tools
Mr. Pedro Marques, von Karman Institute

10:30 Coffee Break

11:00 Lec 6: Bio-Inspired Optimization: Genetic Algorithms and Particle Swarms
Prof. Miguel Mendez, von Karman Institute

12:30 Lunch Break

13:30 Lec 7: An Introduction to Genetic Programming
Mr. Joachim Dominique, von Karman Institute

15:00 Coffee Break

15:30 Exercise 2: Aeroacoustics Noise Prediction with Genetic Programming
Mr. Joachim Dominique, von Karman Institute

17:00 End of day

Wednesday 9 February 2022: Regression Methods from Machine Learning

09:00 Lec 9: Linear Tools for Nonlinear Regression and Super Resolution
Prof. Miguel Mendez, von Karman Institute

10:30 Coffee Break

11:00 Lec 10: The Bayesian Formalism: Gaussian Processes
Prof. Miguel Mendez, von Karman Institute

12:30 Lunch Break

13:30 Lec 11: Deep Learning and Physics Informed Artificial Neural Networks
Mr. Jan Van den Berghe, von Karman Institute

15:00 Coffee Break

15:30 Exercise 3: Deep Learning for Turbulence Modeling
Mrs. Matilde Fiore, von Karman Institute

17:00 End of day

Thursday 10 February 2022: Dimensionality Reduction

09:00 Lec 13: Dimensionality Reduction and Autoencoders
Prof. Miguel Mendez, von Karman Institute

10:30 Coffee Break

11:00 Lec 14: The Linear Autoencoder: Principal Component Analysis
Prof. Miguel Mendez, von Karman Institute

12:30 Lunch Break

 13:30 Lec 15: Nonlinear Autoencoders: Manifold Learning, Kernel Methods and ANN
Prof. Miguel Mendez, von Karman Institute

15:00 Coffee Break

15:30 Exercise 4: Autoencoding and Forecasting Flow Fields
Prof. Miguel Mendez, von Karman Institute

17:00 End of day

Friday 11 February 2022: Flow Control via Machine Learning

09:00 Lec 17: Fundamentals of Flow Control
Prof. Miguel Mendez, von Karman Institute

10:30 Coffee Break

11:00 Lec 18: Reinforcement Learning, Part I
Mr. Romain Poletti, von Karman Institute

12:30 Lunch Break

13:30 Lec 19: Reinforcement Learning, Part II
Mr. Fabio Pino, von Karman Institute

15:00 Coffee Break

15:30 Exercise 5: Flow Control via Genetic Programming and Reinforcement Learning
Mr. Romain Poletti and Mr. Fabio Pino, von Karman Institute

17:00 End of day

Eligibility Criteria

The citizens of the following countries are eligible to attend the von Karman Institute Lecture Series:

- EU member countries
- NATO member countries
- NATO's Mediterranean Dialogue (Algeria, Egypt, Israel, Jordan, Mauritania, Morocco, Tunisia)
- Istanbul Cooperation Initiative (Bahrain, Kuwait, Qatar, United Arab Emirates)
- Argentina, Australia, Bolivia, Brazil, Cabo Verde, Cameroon, Chile, Colombia, Japan, India, Indonesia, Macedonia, Malaysia, Mauritania, Mexico, Montenegro, Mozambique, New Zealand, Nigeria, Republic of Korea, Saudi Arabia, Serbia, Singapore, South Africa, Switzerland, Thailand, Uruguay, South-Africa and Vietnam.

VKI reserves the right to request a clearance check with Belgian authorities.

The participants will have to present their ID card (for EU citizens) or passport (for other citizens) the first day of the lecture series.

Fee and Registration

Coming soon

Course Speakers

 

Aeroacoustics

Prof. Miguel Mendez

Aeroacoustics

Mr. Julien Christophe

Aeroacoustics

Mr. Federico Torres Herrador

Aeroacoustics

Mr. Pedro Marques

Aeroacoustics

Mr. Joachim Dominique

Aeroacoustics

Mr. Jan Van den Berghe

Aeroacoustics

Mrs. Matilde Fiore

Aeroacoustics

Mr. Romain Poletti

Aeroacoustics

Mr. Fabio Pino

Location : von Karman Institute for Fluid Dynamics, Waterloosesteenweg 72, B-1640 Sint-Genesius-Rode