Resumen: In 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. Idioma: Inglés DOI: 10.1007/s00466-019-01705-3 Año: 2019 Publicado en: COMPUTATIONAL MECHANICS 64 (2019), 511–523 ISSN: 0178-7675 Factor impacto JCR: 3.459 (2019) Categ. JCR: MECHANICS rank: 25 / 136 = 0.184 (2019) - Q1 - T1 Categ. JCR: MATHEMATICS, INTERDISCIPLINARY APPLICATIONS rank: 13 / 105 = 0.124 (2019) - Q1 - T1 Factor impacto SCIMAGO: 1.612 - Applied Mathematics (Q1) - Computational Mathematics (Q1) - Ocean Engineering (Q1) - Computational Theory and Mathematics (Q1) - Mechanical Engineering (Q1) - Computational Mechanics (Q1)