doi:10.1007/s00466-019-01705-3engMoya, B.Gonzalez, D.Alfaro, I.Chinesta, F.Cueto, E.Learning slosh dynamics by means of dataART-2019-111497In this work we study several learning strategies for fluid sloshing problems based on data. In essence, a reduced-order model of the dynamics of the free surface motion of the fluid is developed under rigorous thermodynamics settings. This model is extracted from data by exploring several strategies. First, a linear one, based on the employ of Proper Orthogonal Decomposition techniques is analyzed. Second, a strategy based on the employ of Locally Linear Embedding is studied. Finally, Topological Data Analysis is employed to the same end. All the three distinct possibilities rely on a numerical integration scheme to advance the dynamics in time. This thermodynamically consistent integrator is developed on the basis of the General Equation for Non-Equilibrium Reversible–Irreversible Coupling, GENERIC [M. Grmela and H.C Oettinger (1997). Phys. Rev. E. 56 (6): 6620–6632], framework so as to guarantee the satisfaction of first principles (particularly, the laws of thermodynamics). We show how the resulting method employs a few degrees of freedom, while it allows for a realistic reconstruction of the fluid dynamics of sloshing processes under severe real-time constraints. The proposed method is shown to run faster than real time in a standard laptop.2019http://zaguan.unizar.es/record/14913010.1007/s00466-019-01705-3http://zaguan.unizar.es/record/149130oai:zaguan.unizar.es:149130info:eu-repo/grantAgreement/ES/DGA/T24-17RCOMPUTATIONAL MECHANICS 64 (2019), 511–523All rights reservedhttp://www.europeana.eu/rights/rr-f/info:eu-repo/semantics/openAccess