4 papers · 1 filter
Learning with Physical Constraints
Miguel A. Mendez, Jan van Den Berghe, Manuel Ratz +2
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-resolut…
Data-driven turbulent heat flux modeling with inputs of multiple fidelity
Matilde Fiore, Enrico Saccaggi, Lilla Koloszar +2
Data-driven RANS modeling is emerging as a promising methodology to exploit the information provided by high-fidelity data. However, its widespread application is limited by challe…
Challenges and Opportunities for Machine Learning in Fluid Mechanics
M. A. Mendez, J. Dominique, M. Fiore +3
Big data and machine learning are driving comprehensive economic and social transformations and are rapidly re-shaping the toolbox and the methodologies of applied scientists. Mach…
Physics-constrained machine learning for thermal turbulence modelling at low Prandtl numbers
Matilde Fiore, Lilla Koloszar, Miguel Alfonso Mendez +2
Liquid metals play a central role in new generation liquid metal cooled nuclear reactors, for which numerical investigations require the use of appropriate thermal turbulence model…