Low-dimensional Flow Models from high-dimensional Flow data with Machine Learning and First Principles

TL;DR

This paper presents a framework combining machine learning and first principles to derive sparse, interpretable low-dimensional models from complex high-dimensional fluid flow data, enhancing understanding and analysis.

Contribution

It introduces a novel approach that integrates machine learning with fundamental principles to produce interpretable reduced-order models from high-dimensional flow data.

Findings

Successfully derives sparse models from complex data

Enhances interpretability of fluid flow models

Bridges machine learning with physics-based modeling

Abstract

Reduced-order modelling and system identification can help us figure out the elementary degrees of freedom and the underlying mechanisms from the high-dimensional and nonlinear dynamics of fluid flow. Machine learning has brought new opportunities to these two processes and is revolutionising traditional methods. We show a framework to obtain a sparse human-interpretable model from complex high-dimensional data using machine learning and first principles.