公开(公告)号：US20240176935A1

公开(公告)日：2024-05-30

申请号：US18549488

申请日：2021-03-10

Applicant: EXTRALITY

Inventor： Ahmed MAZARI ,  Thibaut MUNZER ,  Louis VERRET ,  Pierre YSER

IPC: G06F30/28 ,  G06F30/23 ,  G06F30/27 ,  G06F111/10 ,  G06F113/08

CPC classification number: G06F30/28 ,  G06F30/23 ,  G06F30/27 ,  G06F2111/10 ,  G06F2113/08

Abstract: A method for numerical simulation of a flow of a fluid in a space around a geometry, implemented by computer, including: a so-called deterministic sampling step, configured to process the initial mesh M0 as an input so as to obtain a set of so-called simulation multiscale meshes, the set of simulation messages including a number Z≥1 of subsampled meshes Mi; a step of generating a simulation result from at least one machine learning algorithm of the neural network type, previously trained from a database including 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.

公开(公告)号：US20240211664A1

公开(公告)日：2024-06-27

申请号：US18556950

申请日：2021-04-26

Applicant: EXTRALITY

Inventor： Louis VERRET ,  Ahmed MAZARI ,  Thibaut MUNZER ,  Pierre YSER

IPC: G06F30/28

CPC classification number: G06F30/28

Abstract: The disclosure relates to a neural network configured for a numerical simulation of a physical phenomenon, such as a fluid flow, a thermal transfer or a calculation of a mechanical structure, by joint learning from physical data of several types correlated with each other from a plurality of numerical training simulations.

公开(公告)号：US20230014067A1

公开(公告)日：2023-01-19

申请号：US17787314

申请日：2020-12-16

Applicant: EXTRALITY

Inventor： Pierre YSER ,  Hainiandry RASAMIMANANA

IPC: G06F30/28 ,  G06F30/27

Abstract: A computer-implemented numerical simulation method for studying a physical system governed by at least one differential equation such as a fluid in motion. The simulation is launched, making it possible to define a simulation domain. In the computation step, a machine learning algorithm is implemented to predict a global solution to the equation in the simulation domain. The computation step includes n consecutive sequences, each sequence includes cutting a piece in the simulation domain followed by predicting a local solution in the piece on the basis of local boundary conditions, n being an integer strictly greater than 1. The prediction step being carried out by a machine learning model, as input, global boundary conditions on the simulation domain.