Learning with Physical Constraints
Fluid Dynamics
2025-12-02 v1 Machine Learning
Machine Learning
Abstract
This chapter provides three tutorial exercises on physics-constrained regression. These are implemented as toy problems that seek to mimic grand challenges in (1) the super-resolution and data assimilation of the velocity field in image velocimetry, (2) data-driven turbulence modeling, and (3) system identification and digital twinning for forecasting and control. The Python codes for all exercises are provided in the course repository.
Cite
@article{arxiv.2512.00104,
title = {Learning with Physical Constraints},
author = {Miguel A. Mendez and Jan van Den Berghe and Manuel Ratz and Matilde Fiore and Lorenzo Schena},
journal= {arXiv preprint arXiv:2512.00104},
year = {2025}
}
Comments
Chapter 3 from Machine Learning for Fluid Dynamics (ISBN 978-2875162090). Based on the VKI-ULB lecture series ''Machine Learning for Fluid Dynamics,'' held in Brussels in February 2022