22 results about "Sparse grid" patented technology

The invention discloses a high-precision adaptive numerical simulation method and system for solving a nonlinear integral-differential equation, and relates to the technical field of computer simulation and numerical calculation. Comprising the steps of setting simulation conditions; constructing a grid space based on the computational domain; constructing a global cubic spline interpolation function based on the solutions on the grid points; if the updated grid point location exists, calculating a right-end integral item by using an adaptive numerical simulation integral algorithm; calculating a heuristic solution and an error estimation value; dynamically adjusting the tentative step length and updating the time according to the estimated value and the ODE tolerance; checking gradient and curvature distribution of solutions, encrypting grids for areas with violent solution changes, and sparse grids for areas with smooth solution changes; using a global cubic spline interpolation function to demap the old grid space to the adjusted new grid space; and iteratively executing the steps, and outputting an integral solution in the computational domain. And finally, collaborative improvement of integral simulation calculation in precision, efficiency and stability is realized.

This paper presents a sparse grid polynomial-based orbital game situation analysis method, belonging to the field of aerospace orbital adversarial and intelligent decision analysis technology. Addressing the problems of lack of systematic representation of multi-source uncertainties, low computational efficiency of traditional quantification methods, and difficulty in achieving multi-objective equilibrium optimization in existing high-orbit multi-to-multi dynamic adversarial scenarios, this paper constructs a POMDP multi-to-multi dynamic game model incorporating uncertainties in high-orbit dynamics and control execution, strategy behavior, and communication delay. A surrogate model is established using sparse grid sampling and polynomial chaotic expansion, and global sensitivity analysis is performed. Based on this, a multi-objective optimization model is constructed, and the Pareto optimal policy set is solved. This achieves game strategy generation that balances performance, robustness, and economy, and is applicable to spacecraft strategy optimization and space situation analysis in high-orbit multi-to-multi dynamic adversarial missions.

The invention relates to the technical field of radiation field calculation, and relates to an improved non-uniform radiation field grid division method and system. Comprising the following steps: carrying out initial uniform grid setting on a radiation field calculation area; performing N-time sparse processing on the initially set uniform grid; calculating the distance from the center point of each sparse grid to the ellipsoid surrounded by the source item, and determining the weight multiple of the grid according to the distance; traversing all source items and grids in sequence; carrying out refining processing on all the grids with the weight multiples greater than 1 according to the corresponding multiples; and summarizing and outputting all grid intersection points, namely a final radiation field calculation dot matrix. According to the radiation field change rule of the area near the source item, the judgment basis of whether the grid needs to be refined is improved, and whether the ratio of the distance from the grid center point to the ellipsoid surrounded by the source item to the source linearity is smaller than 1.5 is modified from whether the grid is an extension grid, so that grid division near the source item is more reasonable, and the accuracy of grid division is improved. And the calculation efficiency and the calculation precision of the radiation field are better considered.

This invention discloses a structural topology optimization design method considering the uncertainty of multi-peak loads, belonging to the field of uncertain structural optimization design. It mainly includes three parts: establishing a multi-peak load uncertainty model, solving for the random response, and solving for the optimization formula. This invention describes load uncertainty using a Gaussian mixture model, solves for the Gaussian mixture model coefficients based on the EM algorithm, and establishes a multi-peak load distribution probability model. By decorrelating random variables, sparse grid technology is used to solve for the mean, standard deviation, and sensitivity of the response, thereby solving for the topology optimization model considering the multi-peak load uncertainty. This method addresses the problem of low structural product reliability that may occur in structural designs considering multi-peak load uncertainty by establishing an accurate probabilistic model of multi-peak load uncertainty. This method is simple and easy to implement, readily applicable in engineering, and can improve the design efficiency of structural designers considering complex load conditions.

The application discloses a kind of calculation methods for simulating toxic and harmful gas diffusion concentration in three-dimensional virtual training environment, S1, determine toxic gas diffusion effective influencing factor;S2, the physical model corresponding to training environment is constructed, and the effective influencing factor of gas diffusion is derived;S3, according to gas toxic dose and shelter size, sparse grid is divided, and grid density is distinguished;S4, CFD model is constructed, and the concentration of simulated harmful gas diffusion is calculated;S5, concentration value time sequence situation result offline database of a period of time is established, and gas concentration situation result is stored;S6, when training, in virtual environment, quickly display.The method of the present application can consider terrain, building, meteorological conditions and other factors affecting gas concentration diffusion, can measure the gas diffusion concentration value of any point in space in real time, and its diffusion concentration value is realistic and practical, so that the simulation effect of harmful gas diffusion concentration value in virtual reality chemical hazard simulation training environment is more realistic and credible, and the training effect is improved.

The application discloses a water area adaptive underwater image enhancement method, which comprises the following steps: 1) based on a feature extraction method of multiple groups of convolution and sparse grid attention, an input underwater image is encoded to obtain encoded features of local details and global color contour information; 2) the domain difference of the underwater environment is constructed as a water area classification problem, and the difference information between different water areas is used to generate water area sensitive features to realize image enhancement of different water areas; 3) based on the enhanced image generated by the water area sensitive features, an aggregation loss and a multi-stage training strategy are used to constrain the enhanced result to realize high-quality underwater image enhancement. The water area adaptive method disclosed by the application fully utilizes the domain difference between different water areas and the difference of local and global information, and realizes adaptive and high-quality underwater image enhancement.

A kind of underwater vehicle noise prediction correction method based on bayesian inference, calculation model (1) includes Corcos autospectrum and Corcos normalized cross power spectral density function calculation model;According to calculation model (1), construct the schematic diagram (2) of bayesian network, the key factor identification and state analysis of the key factor of the modeling of the influencing factor of turbulent fluctuation pressure in bayesian network in schematic diagram (2) of bayesian network are built-in bayesian inference;According to the schematic diagram (2) of bayesian network, model in NETICA software, construct directed acyclic bayesian network (3);Reference calculation model (1) and directed acyclic bayesian network (3) are simulated setting (4) and noise signal comparison (5).The present application has the advantages of ensuring the precision of low-frequency noise prediction in sparse grid large size, improves the problem that the grid scale of finite element model and the space related scale of turbulent boundary layer pressure do not match and the calculation amount is too large when fine grid is used.

This invention discloses an exploration grid optimization method and system. The method includes: reading N original boundary sampling coordinates of a target mining area, constructing a mining area constraint boundary of the target mining area, obtaining the current grid spacing pairs of the mining area constraint boundary, dividing the mining area constraint boundary into grids with the current grid spacing pairs as the step size to obtain J candidate exploration grids, extracting the grid coordinates of the J candidate exploration grids, calculating the parameter variation intensity of the current grid spacing pairs based on the grid coordinates of the J candidate exploration grids, iteratively updating the current grid spacing pairs with a preset step size, and simultaneously calculating the updated parameter variation intensity. If the updated parameter variation intensity is lower than a set threshold, the updated current grid spacing pairs are determined as the optimal grid spacing pairs. This invention can truly reflect the degree of control of the current grid density on the ore body variation, avoiding over-densification or over-sparse grids caused by a lack of quantitative standards.

The invention provides a laser radar point cloud rapid robust ground segmentation method based on an adaptive grid, and the method comprises the steps: dividing an adaptive polar coordinate network for an input point cloud, and obtaining each sector which comprises a basic ring and an adaptive ring; for each sector, performing forward robust first-order filtering on the adaptive ring along the radial direction to obtain the ground height and the radial slope after filtering; according to the ground height and the radial slope obtained after filtering, the filtering estimation height of each point and the deviation between the height of the point and the filtering estimation height are calculated, the deviation is compared with a self-adaptive threshold value for a high-confidence grid unit and a sparse grid unit, and ground point classification is completed; and carrying out ground point classification on the low-confidence grid units by adopting a comprehensive scoring mode again.

The invention discloses a GNN-guided hierarchical H-tree low-skew clock tree synthesis method, which comprises the following steps of: firstly, executing fan-out-radius-capacitance three-constraint clustering on a chip register to generate an optimized buffer leaf cluster; spatial distribution and electrical characteristics are extracted, and an optimal sub-tree division scheme is predicted by using a graph neural network GNN; a K-adaptive non-uniform branch H tree is constructed based on the prediction result, a binary chain structure is adopted for a light load area, and a quadtree structure is adopted for a heavy load area; and finally, integrating the global clock network through the sparse grid. According to the method, the capacitance balance constraint and the graph neural network technology are creatively fused, and compared with a traditional H-tree method, the clock skew is reduced by 11.5%, the clock delay is reduced by 13.9%, the number of buffers is reduced by 31.0%, and the comprehensive operation time is remarkably shortened.

The application provides a curved surface image correction method and device based on multi-modal data fusion, comprising the following steps: S1, projecting a sparse grid laser dot array to a to-be-corrected object, collecting an image of the to-be-corrected object with laser marks, and determining whether it is a high-curvature region according to the interval change of adjacent laser dots; S2, projecting a high-density grid laser dot array to the high-curvature region, collecting the image, obtaining fine depth information of each laser dot to form laser point cloud data; S3, constructing and solving an energy minimization model to generate an inverse distortion mapping field of the corresponding curved surface to a two-dimensional flat plane; and S4, inputting the inverse distortion mapping field to a correction model and outputting a corrected image according to the correction model. The application has the beneficial effects that the laser projection is not affected by the surface texture of the to-be-corrected object and environmental light, the precision and strong robustness are improved, the high-curvature region is pre-screened by using the sparse grid laser, and the working efficiency is improved.

The invention discloses a PINN-based gear point cloud statics stress simulation method and system, and relates to the technical field of solid mechanics simulation analysis. The method comprises the following steps: acquiring and importing a target gear geometric model, performing sparse mesh fast finite element analysis on the target gear geometric model, and exporting global mesh and boundary information; establishing a point cloud model based on the global grid and the boundary information, and determining a boundary condition; and constructing a PINN physical information neural network based on the point cloud model and the boundary condition, constructing a loss function to carry out multiple times of staged network training so as to obtain a prediction result of the PINN, and outputting the prediction result as a simulation result. According to the method, the point cloud data can be directly utilized, accurate prediction of the physical field is realized through a staged training strategy, and the convenience, accuracy and engineering practical value of complex structure analysis are remarkably improved.

The invention provides a method for setting an external dynamic noise source positioning scanning switch and a storage medium, and belongs to the technical field of seismic exploration methods. The method comprises the following steps: (1) acquiring a maximum range area of an exploratory area; (2) acquiring a sparse grid energy spectrum and a sparse energy spectrum grid switch array; (3) randomly extracting a part of shots from the seismic data to serve as sparse shots for energy scanning, and setting a grid scanning switch; according to the method, the positioning calculation efficiency is improved, and the positioning effect is ensured, so that the mass data processing efficiency is improved, the calculation resources are saved, and the purposes of reducing the cost and improving the efficiency are achieved.

The invention specifically relates to a hydrogen-doped natural gas pipeline circumferential weld residual life prediction method, and relates to the technical field of oil and gas pipeline safety engineering, and the method comprises the steps: interpolating a physical quantity of a parent unit to a subunit through a shape function, and completing the data transmission of a state variable after conservative correction verification. According to the method, a self-adaptive adjustment mechanism based on a hydrogen concentration gradient module value is constructed, and collaborative optimization of prediction precision and calculation efficiency is realized; aiming at a core area with violent hydrogen concentration change at the circumferential weld crack tip of the hydrogen-doped natural gas pipeline, grids are encrypted through recursive splitting and a 2: 1 balance rule, the coupling effect of stress concentration and hydrogen atom aggregation is accurately captured, and the problem of key physical field distortion caused by a traditional fixed coarse grid is avoided; for a far-field hydrogen concentration uniform distribution region, sparse grids are coarsened through pruning, and the number of invalid calculation units is reduced.

This application discloses a two-dimensional DC electric field forward modeling method, apparatus, electronic device, dielectric, and product. The method includes: taking the variational problem of a two-dimensional wavenumber domain point source DC electric field as the research object; dividing the model region of the dielectric model into several triangular elements and expanding the model region with a sparse mesh; reading the conductivity and potential of the principal nodes in each triangular element, and representing the conductivity and potential with quadratic interpolation shape functions to obtain a discrete model; constructing a system of linear equations based on the discrete model and the coefficient matrix of each triangular element; solving the system of linear equations to obtain the wavenumber domain forward modeling result; and performing an inverse Fourier transform on the forward modeling result to obtain the spatial domain electric field potential. The method increases the number of nodes in the mesh during model region meshing, reduces computation time by expanding the model, and utilizes more node property information through biquadratic interpolation of conductivity and potential within the mesh, thereby improving the accuracy of the two-dimensional DC electric field forward modeling fitting.

This invention discloses a sparse mesh radiation field representation method based on point cloud initialization and depth supervision. This radiation field can be used for rendering from new perspectives. The method includes obtaining at least two camera color images, obtaining two camera depth images, generating a 3D point cloud from the depth images, and generating rays from each pixel using camera parameters (including intrinsic and extrinsic parameters) to obtain a set of rays for all pixels. Since real-world scenes are often largely blank, this invention uses a sparse mesh to represent the scene, reducing memory costs. It utilizes the completed 3D reconstruction as a geometric constraint to enhance geometric learning capabilities and uses the completed 3D reconstruction as a guide for initialization, reducing the number of parameters during the mesh generation stage, improving optimization efficiency, and reducing storage costs.

This invention relates to the field of compressor blade fouling, and particularly to a two-dimensional geometric uncertainty modeling method for compressor blade fouling. Based on the actual morphology of compressor blade fouling, the fouling is divided into a compact layer and a loose layer along the blade profile normal. Considering the uncertainty of fouling distribution on the compressor blade surface under actual operating conditions, a sparse compact layer fouling geometric uncertainty model is constructed by combining the KL expansion method with a sparse grid numerical integration method, and a randomized coarse loose layer fouling geometric uncertainty model is also established. The geometric uncertainty modeling method for compressor blade fouling provided by this invention is applicable to establishing geometric uncertainty models for compressor blades with arbitrary fouling distributions. The sparse blade fouling geometric model represents all geometric models within the entire distribution space, making it particularly suitable for providing CFD calculation geometric models for predicting the probability of aerodynamic performance degradation in fouled compressor blades.

Various embodiments can perform efficient, large scale fluid simulation using a method, such as the fluid-implicit particle (FLIP) method, over a sparse hierarchy of grids. The grids may be represented in a number of different formats, and may include GVDB Voxels. Such approaches can handle tens of millions of particles within a virtually unbounded simulation domain. Embodiments can utilize a parallel, sparse grid hierarchy construction and provide for fast incremental updates on graphics processing unit (GPU) hardware, for example, for moving particles. In addition, a FLIP-based technique can be used to perform sparse, work-efficient parallel data gathering from particle to voxel. Various embodiments can also utilize a matrix-free GPU-based conjugate gradient solver optimized for sparse grids. Such approaches can provide orders of magnitude faster simulations on GPU hardware with respect to conventional simulations.

A chart for camera calibration is tilted away from the camera. The part of the chart that is further from the camera has a relatively sparse grid to make finding grid corners easier, whereas the part of the chart closer to the camera has a relatively dense grid to provide more data for better calibration.

The invention relates to the technical field of foreign exchange options, and discloses a foreign exchange option pricing method and system, and the method comprises the steps: 1, determining the dimension d of an option pricing model; step 2, when the dimension d is greater than or equal to 2, calculating the number of nodes by adopting sparse grid compression; step 3, identifying a key area based on a preset Gamma sensitivity threshold; step 4, when the dimension is dgt; 3, automatically switching to Monte Carlo simulation; 5, executing sparse grid superposition, dimension reduction decomposition and Monte Carlo path generation tasks in parallel through a graphics processor; according to the method, the high-dimensional calculation complexity is reduced through sparse grid compression, the key area is dynamically encrypted in combination with Gamma sensitivity, and Monte Carlo simulation is automatically switched in ultrahigh dimension, so that the problems of high calculation resource consumption and rigidity in precision adjustment of a traditional method are effectively solved, and the method is suitable for large-scale popularization and application. The method has the advantages of improving the calculation efficiency, dynamically optimizing the key region precision and balancing the calculation resources and precision.

The application provides a cable-supported photovoltaic support vortex vibration response model construction method, and belongs to the technical field of cable-supported photovoltaic support detection. The application extracts modal parameters through fast Fourier transform, performs non-uniform sampling in a six-dimensional parameter space based on a sparse grid point distribution method, performs computational fluid dynamics numerical simulation on the sampling points to establish an aerodynamic force characteristic database, trains a vortex vibration response prediction model to output a vortex-induced lift coefficient, adopts a modal selection criterion based on energy proportion to retain key modes, adopts a symplectic geometry structure-preserving numerical integration algorithm to perform time-domain integration and solving, introduces a corotational coordinate system to perform geometric nonlinear correction, adopts an immersed boundary-lattice Boltzmann coupling method to update a fluid-structure coupling interface, adjusts an artificial intelligence model learning rate through an adaptive adjustment factor, judges convergence, and outputs vortex vibration response results, thereby solving the problem of excessive consumption of resources in vortex vibration response prediction and calculation of cable-supported photovoltaic supports.

The application discloses a double-order-reduced numerical control machine tool digital twin physical field construction method. The physical field finite element simulation sample data set under the working state sampling space is initially reduced to a relatively sparse grid node data through an improved KNN algorithm, so that the number of point sets is kept within the real-time rendering capability range of graphics rendering software; an RBF radial basis function is used to train a digital twin physical field agent model, so that the physical field is reconstructed; a genetic algorithm is used to iteratively optimize the digital twin physical field agent model, so that the deviation between the agent physical field model and the real physical model is solved; and finally, the digital twin physical field agent model is deployed to a digital twin system and used to solve the physical field distribution in real time. The application reflects the spatial distribution of the structure field and the temperature field in the digital twin system, so that the real-time operation data driven virtual-real image mapping physical field simulation is realized.