Data Processing and Machine Learning

FLUID ENGINEERING AND MEASUREMENT

Objectives

Lead the ongoing big data revolution in fluid dynamics and its integration with classic methods
Align with latest developments in Artificial Intelligence for data analytics, forecasting and adaptive control
Support the other research with expertise, inhouse algorithms, tools and data driven methods

Meshless derivation from Lagrangian Particle Tracking via Radial Basis Funcions of Pressure fields

Topics

Data Driven Modeling and Regression
Dimensionality and Model Order Reduction
Time Series Analysis and Forecasting
Adaptive Feedback Control

BOOK

Mendez MA, Ianiro A, Noack BR, Brunton SL, eds. Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning. Cambridge University Press; 2023.

Online ISBN:9781108896214 / DOI:https://doi.org/10.1017/9781108896214

Buy the book

Data-driven methods have become an essential part of the methodological portfolio of fluid dynamicists, motivating students and practitioners to gather practical knowledge from a diverse range of disciplines. These fields include computer science, statistics, optimization, signal processing, pattern recognition, nonlinear dynamics, and control. Fluid mechanics is historically a big data field and offers a fertile ground for developing and applying data-driven methods, while also providing valuable shortcuts, constraints, and interpretations based on its powerful connections to basic physics. Thus, hybrid approaches that leverage both methods based on data as well as fundamental principles are the focus of active and exciting research. Originating from a one-week lecture series course by the von Karman Institute for Fluid Dynamics, this book presents an overview and a pedagogical treatment of some of the data-driven and machine learning tools that are leading research advancements in model-order reduction, system identification, flow control, and data-driven turbulence closures.