Eleni Koronaki: “Dinky, Dirty, Dynamic & Deceptive Data (1)”: An overview of hybrid machine learning and equation-based modelling

Machine Learning Seminar presentation

Topic: “Dinky, Dirty, Dynamic & Deceptive Data (1)”: An overview of hybrid machine learning and equation-based modelling

Speaker: Dr. Eleni Koronaki, University of Luxembourg, Department of Computational Science

Time: Wednesday 2020.10.21, 10:00 CET

How to join: Please contact Jakub Lengiewicz

Abstract:

In the era of “Big Data”, machine learning frameworks are attractive candidates for leveraging abundant data and transforming it into meaningful information. Despite the success of methods such as Deep Neural Networks in diverse sectors, ranging from finance to healthcare and language recognition, to name just a few, their implementation in traditional engineering fields is not universal. The reason is twofold: Firstly, first principles-based models, albeit computationally expensive remain consistent decision-making tools, more so now with the evolution of computational algorithms and infrastructure. Secondly, in many applications, the available data is not “big” enough to ensure accuracy and reliability of machine learning workflows. This dichotomy has not been unnoticed in the engineering community and various efforts to address this have been published, surprisingly as early as in the early 90s.

Nowadays the advent of Physics-Informed Neural Networks (PINNs) revisits older concepts with remarkable results. In this presentation, an illuminating overview is attempted of the “hybrid physics-informed machine learning” paradigm.

Additional material:

(1) Dr. A. Kott