Learning macroscopic equations of motion from dissipative particle dynamics simulations of fluidsJug, Matevž (Avtor) Svenšek, Daniel (Avtor) Potisk, Tilen (Avtor) Praprotnik, Matej (Avtor) sparsitymodel selectionparticle simulationsmacroscopic dynamicsregressionMacroscopic descriptions of both natural and engineered materials usually include a number of phenomenological parameters that have to be estimated from experiments or large-scale microscopic simulations. When dealing with advanced complex materials, these descriptions are sometimes not a priori available or not even known. Using sparsity-promoting techniques one can extract macroscopic dynamic models directly from particle-based simulations. In this work, we showcase such an approach on a simple fluid and test its robustness. We introduce a novel measure for automatic macroscopic model selection that combines stability and accuracy of a model. Using this measure and employing only a few physics-based assumptions, we are able to infer both the mass continuity equation and an equation for the conservation of linear momentum. Moreover, the extracted phenomenological and non-phenomenological parameters agree well with their numerically measured values and the well-known semi-empirical estimates. The presented model selection framework can be applied to simulations or experimental data of more complex systems, described in general by a rich set of coupled nonlinear macroscopic equations.20242024-09-27 08:37:25Članek v reviji162771sl