Method for the numerical simulation of unstructured data through multiscale machine learning and deterministic sampling

Method, implemented by computer, for numerical simulation of a flow of a fluid in a space around a geometry on which an initial mesh M0 is defined, said initial mesh being divided into a number N of geometric elements, each geometric element being of the surface or volume type, the numerical simulation method being characterized in that it comprises:

• a so-called deterministic sampling step configured to process said initial mesh M0 as an input so as to obtain a set of so-called simulation multiscale meshes, said set of simulation messages comprising a number Z≥1 of subsampled meshes Mi, with i integer between 1 and Z, in accordance with the following criteria:
• the subsampled mesh or meshes Mi each include a number ni of groups of geometric elements of said initial mesh, such that ni being strictly less than N, each ni being distinct,
• each subsampled mesh Mi being defined over the whole of said space,
• the relationships between the groups of geometric elements of a subsampled mesh Mi and of a subsampled mesh Mi+1 being stored in an indexing table Xi,i+1,
• the number of subsampled meshes Mi and the number ni of groups of geometric elements of each subsampled mesh Mi being predetermined parameters of said deterministic sampling step;
• a step of generating a simulation result from at least one machine learning algorithm of the neural network type, previously trained from a database comprising a plurality of sets of so-called training multiscale meshes each associated with a numerical simulation, to provide a set of simulation data for all or some of the nodes of a mesh Mi in the set of multiscale meshes obtained during the deterministic sampling step.