Trends and Challenges in Scientific Machine Learning for Fluid Dynamics

Ahmed Mazari

Speaker: <a href="https://www.mendezma.com/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Miguel Alfonso Mendez</a>, Associate Professor at the <a href="https://www.vki.ac.be/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">von Karman Institute for Fluid Dynamics (VKI)</a>Title: Trends and Challenges in Scientific Machine Learning for Fluid DynamicsB﻿iography: M. A. Mendez received his PhD in engineering science from “Université Libre de Bruxelles” in 2018. He is currently an Associate Professor at the von Karman Institute for Fluid Dynamics, where he teaches courses on modelling and control of fluid flows, measurement techniques, signal processing and machine learning. He has extensively used data-driven methods for post-processing numerical and experimental data. His main research activities include experimental fluid mechanics (particularly image-based velocimetry and image processing), reduced-order modelling, flow control, and machine learning. A﻿bstract:The use of machine learning in fluid dynamics is continuously growing and evolving. Machine learning offers robust tools to identify patterns and learn functions from large datasets, and the ever-growing availability of experimental and numerical data makes it viable also for disciplines traditionally rooted in physical principles, mechanistic modelling and differential equations such as fluid dynamics. Blending machine learning and physical modelling is the essence of scientific machine learning, an emerging field with promising avenues in engineering and applied sciences. This talk overviews how physical priors can be embedded into machine learning models and discusses some major trends and perspectives in inverse modelling and control of fluid flows. Papers to be discussed: <ul><li>Physics-constrained machine learning for thermal turbulence modelling at low Prandtl numbers</li></ul><ul><li>Artificial neural networks modeling of wall pressure spectra beneath turbulent boundary layers</li></ul><ul><li>Control of a Wind-Turbine via Machine Learning techniques</li></ul><ul><li>Turbulent heat flux modelling in forced convection flows using artificial neural networks</li></ul><ul><li>Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning</li></ul>

Trends and Challenges in Scientific Machine Learning for Fluid Dynamics

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Trends and Challenges in Scientific Machine Learning for Fluid Dynamics

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