5 results about "Property modeling" patented technology

The invention relates to the technical field of cable assembly simulation, in particular to a cable physical property modeling method based on multi-physical parameter fusion, which deeply fuses multi-dimensional physical parameters of a cable, namely mechanical property parameters, material attribute parameters and external force action parameters, and constructs a multi-physical parameter coupled cable physical property model. Accurate description of cable mechanical behaviors is realized, and rapid solving of a cable physical property model is realized based on technologies such as GPU acceleration. Firstly, the model is processed, so that solving difficulty caused by equality constraint and integral calculation is avoided; then, through exploration, determining that a Newton method and a nonlinear least square method are respectively used for solving a cable physical property model with external force and without external force; and finally, the solving efficiency of the cable physical property model is greatly improved by utilizing the parallel computing capability of the GPU.

An integrated process-structure-property modeling framework for design optimization and / or performance prediction of a material system includes a powder spreading model using a discrete element method (DEM) to generate a powder bed; a thermal-fluid flow model of the powder melting process to predict voids and temperature profile; a cellular automaton (CA) model to simulate grain growth based on the temperature profile; and a reduced-order micromechanics model to predict mechanical properties and fatigue resistance of resultant structures by resolving the voids and grains.

This invention discloses a reservoir attribute modeling method and apparatus, comprising: generating a reservoir lithology distribution model based on a sedimentary facies training template library, a lithology training template library, and a first adversarial generative network (AGN), wherein the first AGN is a depth convolutional AGN based on multi-level well-seismic co-processing, and using preset-type sedimentary facies training template data selected from the sedimentary facies training template library as the initial input data for the network; generating a reservoir attribute model based on well logging attribute data of the work area, the reservoir lithology distribution model, and a second AGN, wherein the second AGN is a geological attribute generation network based on lithology constraints; and correcting the reservoir attribute model based on the work area production dynamic data, using a reservoir attribute model processed by a reservoir numerical simulation surrogate, and a third AGN, wherein the third AGN is an attribute optimization network based on dynamic constraints. This invention can effectively improve the dynamic and static data consistency rate of random reservoir attribute modeling.

The application discloses a kind of based on multi-dimensional dynamic scale soil property intelligent prediction method and system, it is related to soil prediction technical field, including: acquisition remote sensing image, terrain factor, meteorological factor and soil measured sample data, unified storage in multi-source grid data warehouse;Multi-source data is preprocessed, and unified format grid data is constructed;Combined with topographic difference, land use and climate fluctuation, dynamically divide spatial scale, time scale, topographic stratification scale and land use pattern scale, construct multi-dimensional dynamic scale feature expression vector;Based on multi-task deep neural network model, soil property modeling and training are carried out;The model is applied to unmeasured area, and output prediction result and spatial distribution layer.The system includes multi-source grid data construction and management module, multi-dimensional dynamic scale feature construction module and soil property modeling and prediction module.The application can be used in forestry management and protection, resource management, ecological monitoring and resource evaluation etc.

A method implements property modeling using attentive neural processes. The method includes receiving sparse context input comprising a plurality of context locations corresponding to a plurality of geological property values for a geological property and selecting a plurality of target locations in a space of the plurality of context locations. The method further includes generating a predicted mean image for the geological property by an attentive neural process model using the plurality of target locations and the sparse context input and presenting the predicted mean image.