Hands on Machine Learning for Fluid Dynamics 2023
Date: Feb. 13, 2023 - Feb. 17, 2023
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: 3/02/2023
Registration deadline for online participation: 13/02/2023
A "questions and answers" session of 15 minutes is organized after each lectures.
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
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
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
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
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
- 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, Bosnia-Herzegovina, 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.
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 |
Special Registration (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 |
*Fees are VAT included.
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