von Karman Institute Lecture Series and Events

Hands on Machine Learning for Fluid Dynamics 2023

Monday 13 February 2023 - Friday 17 February 2023

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 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).

Location (online / 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.

Programme (CET)

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

Monday 13 February 2023: Introduction and Fundamental of Optimization

08:00 Registration

08:45 Welcome Address and Course Introduction

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

10:30 Coffee Break

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

12:30 Lunch Break

13:30 Lecture 3: A Review of Optimization Tools
Mr. Pedro Marques, von Karman Institute

15:00 Coffee Break

15:30 Lecture 4: Bio-Inspired Optimization: Genetic Algorithms and Particle Swarms
Prof. Miguel Mendez, von Karman Institute

17:00 End of day

Tuesday 14 February 2023: Regression Methods from Machine Learning

09:00 Lecture 5: Linear Tools for Nonlinear Regression and Super Resolution
Prof. Miguel Mendez, von Karman Institute

10:30 Coffee Break

11:00 Lecture 6: The Bayesian Formalism and Gaussian Processes
Prof. Miguel Mendez, von Karman Institute

12:30 Lunch Break

13:30 Lecture 7: An Introduction to Genetic Programming
Mr. Lorenzo Schena, von Karman Institute

15:00 Coffee Break

15:30 Lecture 8: Artificial Neural Networks and Deep Learning
Mr. Jan Van den Berghe, von Karman Institute

17:00 End of day

Wednesday 15 February 2023: Regression Methods and Physics-based Modeling

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

10:30 Coffee Break

11:00 Lecture 10: Exercises on Deep Learning for Turbulence Modeling
Mr. Samuel Ahizi, von Karman Institute

12:30 Lunch Break

13:30 Lecture 11: Data Assimilation and Inverse Methods
Prof. Miguel Mendez, von Karman Institute

15:00 Coffee Break

15:30 Lecture 12: Neural ODES and Dynamical Systems
Mr. Samuel Ahizi and Mr. Jan Van den Berghe, von Karman Institute

17:00 End of day

Thursday 16 February 2023: Dimensionality Reduction

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

10:30 Coffee Break

11:00 Lecture 14: The Linear Autoencoder: Principal Component Analysis
Prof. Miguel Mendez and Mr. Pedro Marquez, von Karman Institute

12:30 Lunch Break

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

15:00 Coffee Break

15:30 Lecture 16: Exercises on Autoencoding and Efficient Space-Time Regression
Mr. Pedro Marques and Mr. Jan Van den Berghe, von Karman Institute

17:00 End of day

Friday 17 February 2023: Flow Control via Machine Learning

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

10:30 Coffee Break

11:00 Lecture 18: Reinforcement Learning, Part I
Mr. Fabio Pino, von Karman Institute

12:30 Lunch Break

13:30 Lecture 19: Reinforcement Learning, Part II
Mr. Romain Poletti, von Karman Institute

15:00 Coffee Break

15:30 Lecture 20: Exercises on Flow Control with Machine Learning
Mr. Lorenzo Schena and Mr. Romain Poletti, 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 course.

Fee and Registration

A discount of 50% is applied for an online participation

Early Bird registration deadline: 13/12/2022

Registration deadline for on site participation: 3/02/2023

Registration deadline for online participation: 6/02/2023

Price for citizens of NATO countries

	Early registration	Late registration
Undergraduate students	221	245
PhD students	605	672
Staff from recognized universities / research center	958	1064
Staff from commercial organizations	1210	1344

Price for citizens of other countries

	Early registration	Late registration
Undergraduate students	315	350
PhD students	864	960
Staff from recognized universities / research center	1368	1520
Staff from commercial organizations	1728	1920

Information for PhD candidates and undergraduate students: To obtain the reduced price, the applicant must provide a recommendation letter from his or her professor; if not, the request will not be taken into consideration.