5results about How to "Simulation speed" patented technology

The invention relates to the technical field of numerical simulation, in particular to a grid model generation method for the fluid displacement process of well cementation engineering. The method comprises the following steps: acquiring geological data of a well cementation project area, and constructing an initial geological model; well cementation engineering design data are obtained, well cementation engineering topological structure modeling is carried out, and a well cementation engineering topological structure model is obtained; according to the initial geologic model and the well cementation engineering topological structure model, fluid dynamic simulation is carried out, and a fluid characteristic matrix is obtained; performing fluid displacement process simulation according to the fluid characteristic matrix to obtain fluid displacement process data; performing fluid displacement process optimization on the well cementation engineering design data based on the fluid displacement process data to obtain optimized fluid displacement process data, and performing resource allocation redistribution on the well cementation engineering design data according to the optimized fluid displacement process data to obtain optimized well cementation engineering design data. According to the invention, the accuracy of fluid displacement simulation can be improved.

The invention discloses a three-dimensional seismic simulation method based on a residual Fourier neural operator. The invention provides an embedded physics data-driven deep learning seismic wave field simulation technology. According to the method, a Fourier wave propagation operator structure is embedded into a convolutional neural network structure, and wave propagation simulation is alternately carried out in a spatial domain and a wavenumber domain. Due to the fact that the network structure conforming to the wave field propagation mode is adopted, and the implied wave field propagation mechanism in the data can be automatically met, the efficiency of the deep learning seismic simulation method is greatly improved, the requirements of the deep learning seismic simulation method for the training data volume and the training time are reduced, and meanwhile the generalization performance of a deep learning model can be enhanced. Compared with a classic finite difference numerical solution, the method has the advantages that the operation efficiency is higher, the occupied storage space is smaller, the method is not restrained by the space grid size and the time step length, and the method is more suitable for large-range high-frequency complex medium seismic wave simulation requirements.

The invention discloses a quantum state amplitude coding method and device based on a Gray code, equipment and a medium. The method comprises the following steps: carrying out normalization processing on a real vector to be coded; generating a Gray code queue according to the normalized real vector to be coded; according to the generation sequence of each Gray code in the Gray code queue, calculating any two adjacent Gray codes to obtain a corresponding target XOR value; encoding the normalized real vector to be encoded by adopting a quantum state encoding algorithm to obtain a parameter of an Ry gate; constructing a target quantum circuit according to the Gray code queue; the target quantum circuit comprises a plurality of X gates and Ry gates, and each X gate and each Ry gate are alternately arranged; the application position of each X gate is determined according to a target exclusive-or value; the application position of each Ry gate containing parameters is determined according to each Gray code; and obtaining a target quantum state through target quantum circuit evolution. According to the invention, the amplitude coding efficiency can be effectively improved under the condition that the number of quantum gates using quantum circuits is reduced.

The application discloses a training method, a simulation method, equipment and a medium of a simulation model of combustion fracturing crack propagation. The training method of the simulation model of combustion fracturing crack propagation based on a graph neural network comprises the following steps: obtaining combustion fracturing crack propagation simulation parameters; generating a triangular mesh geometric model according to the geometric model parameters; performing numerical simulation of combustion fracturing crack propagation according to the generated triangular mesh geometric model and the combustion fracturing engineering parameters to obtain displacement data and crack state data of the triangular mesh geometric model at multiple time steps; constructing a graph structure and determining features according to the displacement data and the crack state data of the triangular mesh geometric model at the multiple time steps; taking graph data of the graph structure as a training data set according to the constructed graph structure; and training a first graph neural network model constructed in advance according to the training data set.

The present invention relates to the field of deep learning technology, and in particular to a micromagnetodynamics simulation method based on a generative model. The magnetic moment distribution is predicted by using a generative model, thereby replacing the traditional method of directly solving the LLG equation to directly calculate the magnetic moment. By learning the dynamic evolution process of the LLG equation, this method achieves a rapid prediction of the change of the magnetic moment and significantly improves the computational efficiency. This technology can greatly improve the speed of micromagnetodynamics simulation, aiming to solve the problems of high computational cost and long computational time in the prior art.