Abstract: A method for simulating the combustion of a fluid in a combustion chamber, for the design of said combustion chamber, which includes: discretizing the space of the chamber into a given mesh; training a neural network by means of a learning set associating a graph corresponding to the mesh, the vertices of which have, as a value, progress variables predicted by a computational fluid dynamics simulation with local combustion quantities at these vertices; and an iterative simulation phase, where: the values predicted by the neural network of a local combustion quantity at the vertices of the mesh are provided as input to a solver, in order to obtain a value of a progress variable at each vertex of said mesh, and a graph corresponding to the vertices of the mesh is provided to said neural network, each vertex having a corresponding value of said progress variable, obtained by said solver, in order to obtain predicted values of the local combustion quantity at said vertices.

Abstract: A method for simulating the behavior of a system (20) composed of a subsystem (10), such as an aerial vehicle, in a physical environment (21) made of an incompressible fluid, comprising training a multilayer neural network (HNN) by means of a training set associating coordinates (x, y) in said physical environment (21) and respective values of a gradient of the current function (?) at said coordinates (x,y), and a loss function configured to minimize an error between a gradient (S?) of the output of said neural network and said gradient of the current function; using said neural network to predict a value of a current function (?) and a velocity field (u) by providing it with input coordinates.

Abstract: A method for simulating the behavior of a system (20) composed by a subsystem (10) in a physical environment (21), by a set of modules, each simulating a portion of said system, wherein at least one portion (11) of said subsystem (10) is simulated by a module implementing a multilayer neural network, the other modules implementing simulations based on behavioral physical laws; said method comprising the assessment (S2) of the results of the simulation to automatically decide (S3) on a strategy to trigger updates (S4) of parameters associated to said multilayer neural network.

Abstract: The method for preloading data of a file containing the following steps of defining a plurality of bins of predetermined sizes in a file, for each input and/or output operation executed on the file, determining the bin involved in the operation, counting the number of input and/or output operations executed in each bin of the file by taking into account only a predetermined number of last operations on the whole file, and when the sum of the operations counted in a bin is greater than a predetermined threshold, loading, in a memory medium, at least one area of the file determined on the basis of this bin.

Abstract: A method for optimizing the run parameters of a software application on an information processing platform, consisting of iteratively optimizing said parameters on each execution of said application, wherein, for each execution of said application (11), a set of said parameters is determined and a run time of said application with said parameters is determined, and an association between said set and said run time is stored in order to create a history (25); and wherein said set is determined by implementing a genetic optimization algorithm comprising a step (21) consisting of selecting two sets of parameters from said history; a step (22) consisting of creating a new set of parameters by recombining said two sets of parameters; and a step (23) of random mutation of said new set of parameters.

Abstract: The invention relates to a method for optimizing the execution parameters of a software application on an information processing platform, consisting in iteratively optimizing said parameters at each execution of said application, in which: for each execution (1) of said application, performance measurements are made (2) and stored (3), in association with the parameters used for said execution; at the start of each execution of said application, values are determined (4, 6) for a first subset of said parameters by inference (6) from the stored measurements corresponding to a subset of the executions corresponding to a second subset of said parameters.

Abstract: The method for preloading data of a file containing the following steps of defining a plurality of bins of predetermined sizes in a file, for each input and/or output operation executed on the file, determining the bin involved in the operation, counting the number of input and/or output operations executed in each bin of the file by taking into account only a predetermined number of last operations on the whole file, and when the sum of the operations counted in a bin is greater than a predetermined threshold, loading, in a memory medium, at least one area of the file determined on the basis of this bin.