Flow Modeling with Machine Learning
Strömungsmodellierung anhand maschinelles Lernen
6 CPSummerEN
Prerequisites
Recommended, as written
The student should be highly motivated to study numerical simulations of fluid flows using computational methods. This is a Python-based course demanding some relevant programming background. To that extent, successful completion of the module “Introduction to Computational Thinking and Programming for CFD” is highly desired (but not mandatory). Completion of the module “Turbulence Modeling” is also recommended for a better theoretical overview, but not mandatory.
Contents
The course contents offer an overview on key machine learning concepts and fundamentals, and how they can be applied in CFD, mainly in the context of analysis of numerical simulations, and modeling of turbulent flows. • Review of linear algebra, conservation equations, dimensional analysis, and turbulence modeling • Linear regression algorithms and optimization • Fourier and Gabor transforms: links to turbulence modeling • Model selection • Proper Orthogonal Decomposition (POD) and Principal Component Analysis (PCA) • Generalities on neural networks • Clustering and classification • Flow modeling using reduced order models
Learning outcomes
The students are offered an introduction to machine learning in the context of computational fluid dynamics, and turbulent flow analysis and modeling. Elementary definitions and concepts in machine learning will be motivated by CFD applications. A large part of the course is dedicated to the analysis of numerical simulation data using supervised learning approaches. Some aspects of flow feature extraction using unsupervised learning, reduced order modeling, as well as general State-Of-The-Art research issues in machine learning for CFD will also be discussed. At the end of the course, the students are able to understand the key concepts and algorithms in machine learning and how they could be applied in CFD.
Assessment
Prerequisite: • Successful completion of the 4-6 exercises discussed in the course Final Module Examination: • Oral exam (Presentation of short project with consecutive discussion of the results), 30-45 min.
Literature (6)
- Duraisamy, Iaccarino and Xiao. “Turbulence Modeling in the Age of Data”, Annu. Rev. Fluid Mech. 51 (2019), 357-377
- Brunton, Noack and Koumoutsakos. “Machine Learning for Fluid Mechanics”, Annu. Rev. Fluid Mech. 52 (2020), 477-508
- Brunton & Kutz. Data-driven Science and Engineering. Cambridge University Press, 2019.
- Chapra & Canale. Numerical Methods for Engineers. McGraw-Hill Higher Education, 2006.
- Hastie, Tibshirani, and Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2009.
- MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.
Remarks
The module primarily aims at Master students in the engineering and natural sciences who plan to specialize in computational fluid dynamics.
Source: BTU's module description · retrieved 29 Sep 2026Original