5 papers
Predicting Wind Loads on Container Ships in Harbor Environments through Multi-Fidelity Modeling
Matilde Fiore, Andrea Bresciani, Miguel Alfonso Mendez +1
Modern container ships face higher wind loads due to increased windage areas, making accurate predictions of wind loads essential for mooring design. Existing empirical models, lar…
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…