13 results about "Preconditioner" patented technology

A subsurface representation may define subsurface configuration of a subsurface region. A grid connectivity graph for the subsurface representation may include (1) nodes that represent cells within the subsurface representation, and (2) edges between the nodes that represent connectivity between the cells within the subsurface representation. The grid connectivity graph may be filtered to remove edges that do not satisfy a connectivity criterion. The filtered grid connectivity graph may be used to compute a linear solver preconditioner that improves the performance of the subsurface simulation.

This invention discloses a five-dimensional seismic data reconstruction method based on preconditioned Riemann gradient descent, belonging to the field of seismic data processing technology. The method involves performing a Hankel transform on fixed-frequency four-dimensional seismic data to construct a fourth-order block Hankel matrix; performing hard thresholding on the fourth-order block Hankel matrix to obtain a low-rank approximation matrix, and calculating the Euclidean gradient at the low-rank approximation matrix in the current iteration; calculating a preconditioner based on the diagonal part of the Euclidean gradient outer product; calculating the projection of the Euclidean gradient onto the tangent space of the Riemann manifold based on the Euclidean gradient and the preconditioner, and updating the low-rank approximation matrix based on the projected gradient to obtain the low-rank approximation matrix for the next iteration; iterative updates continue until a termination condition is met; the low-rank approximation matrix from the last iteration is inversely transformed into a fourth-order tensor, and the reconstructed five-dimensional seismic data is obtained based on the fourth-order tensor and the frequency components in the five-dimensional seismic data. This method can improve the reconstruction efficiency and quality of five-dimensional seismic data.

The invention discloses a piezoelectric ceramic driver control method based on a multi-grid method, and belongs to the technical field of precise driving control. Aiming at the problems of low positioning precision caused by inherent nonlinear characteristics of hysteresis, creep and the like of a piezoelectric ceramic driver, many parameters of a traditional complex model and large calculation amount, the method comprises the following steps: firstly, establishing an electromechanical coupling nonlinear power system model, and accurately converting an original system into a linear system through a feedback linearization technology; then constructing an optimal control problem, deducing a regular equation set by using a Pontryagin minimum principle, and performing time discretization by using an implicit Euler format to ensure numerical stability; and finally, introducing a multi-grid algorithm to efficiently solve the large-scale discrete system. According to the method, the calculation complexity is remarkably reduced while the model precision is reserved, the convergence speed is increased by more than 8 times compared with a traditional iteration method, the robustness to parameter changes is high, a precondition device does not need to be adjusted, the positioning error can be controlled within the range, and the method is suitable for the high-end manufacturing fields such as precise positioning and optical focusing.

The invention discloses a CFD sparse linear system iteration strategy and precondition intelligent selection method based on structure perception graph embedding, and relates to the technical field of computers, and the method comprises the steps: in a process of executing a target simulation task based on CFD simulation software, employing a residual drive sampling mechanism to collect data, obtaining a physical consistency data set, and obtaining a physical consistency data set; carrying out graph modeling based on the physical consistency data set, and determining a graph embedding vector by utilizing a graph modeling result and a structure sensing graph embedding model; based on a graph embedding vector and a decoupling type integrated decision recommendation system, determining a combined selection result of a CFD sparse linear system iteration strategy and a precondition; and based on the combination selection result and CFD simulation software, completing CFD numerical simulation operation corresponding to the target simulation task. According to the method and the device, the problems in the existing related schemes are effectively solved, so that the efficiency of CFD numerical simulation is improved, and the collapse risk during large-scale simulation is reduced.

The present invention discloses a high-efficiency and stable method for simulating inextensible ropes in crane and hoist. The piecewise linear inextensible rope is usually represented by the Cartesian coordinates of the vertices and the quaternions on the segments, which uses too many degrees of freedom and needs many constraints, bringing unnecessary numerical difficulties and computational burden to the simulation. The present invention proposes a compact representation which uses the minimum number of degrees of freedom and naturally satisfies all the additional constraints. Specifically, the rope is regarded as a chain of rigid segments, and its shape is encoded as the Cartesian coordinates of its root vertex and the axis-angle representation of the local coordinate system on each segment. Under the representation of the present invention, the implicit time-stepping matrix has a special non-zero pattern. Using the non-zero pattern, the present invention designs a preconditioner which can solve the related linear equations with approximate linear complexity, and its speed is improved by one to two orders of magnitude compared with the widely used block-diagonal linear PCG solver.

The invention relates to a linear equation set adaptive solving method and system based on a multi-task perception AI model pool. The method comprises the following steps: acquiring input matrix data and right-end item data of a linear equation set to be solved; performing multi-modal feature extraction on the matrix data and the right-end item data to obtain multi-modal features of a linear equation set; calling a corresponding model according to the context of the current solving environment, automatically analyzing the multi-modal characteristics of the system of linear equations to obtain a multi-order system of linear equations and a multi-order sequence, selecting a proper combination of an iteration method and a precondition sub-, iteration method parameters, precondition sub-parameters and sparse matrix operators, and calculating the multi-order system of linear equations and the multi-order sequence of linear equations. And performing solution option setting on the solver, performing corresponding matrix format conversion and kernel binding, calling the solver after the solution option setting to solve the reorder linear equation set to obtain a solution vector, and performing inverse reorder on the solution vector based on a reorder sequence to obtain a final solution vector. The calculation time is obviously shortened, the solving efficiency is improved, and the hardware resource utilization rate is fully improved.

The invention belongs to the technical field of structural finite element data processing, and particularly relates to an iterative solution method and device for a finite element equation set of a whole machine structure. The method comprises the steps of obtaining a complete machine structure sparse positive definite stiffness matrix and a load vector applied to a complete machine structure, and constructing a complete machine structure finite element equation set used for representing the relation among the complete machine structure sparse positive definite stiffness matrix, the load vector and a displacement vector; performing degree-of-freedom elimination on the sparse positive definite stiffness matrix of the whole machine structure to obtain an independent degree-of-freedom matrix; performing bandwidth optimization sorting on the independent degree-of-freedom matrix to obtain a sorted stiffness matrix; processing the sorted stiffness matrix based on an algebraic multi-grid preprocessor to obtain a preprocessed sub-matrix; and calculating a displacement vector by adopting a conjugate gradient iteration method according to the pre-processing sub-matrix. According to the method, the problem that the sparse matrix of the aviation structure is not converged in iterative solution can be effectively solved, and the solution efficiency is remarkably improved while the calculation precision is ensured.

The application discloses a kind of geometric structure finite element numerical solution method, system, medium and equipment, method includes: obtaining the structure parameter and boundary constraint parameter of geometric structure;According to structure parameter and boundary constraint parameter, construct the geometric model of geometric structure;Geometric model is carried out grid discretization processing, obtain the discretization parameter of geometric model and the geometric grid sequence of the composition geometric model, geometric grid sequence includes multiple hierarchical geometric grid;According to discretization parameter, based on summation decomposition constructs the matrixless operator of geometric grid, the matrixless operator is used to directly execute matrix-vector multiplication calculation in geometric model solving process;According to matrixless operator and geometric grid sequence, construct hybrid preconditioner;According to hybrid preconditioner, solve geometric model, obtain the solution result of geometric structure.It can reduce memory occupation, improve solving efficiency and reduce calculation cost.

Systems, methods, and computer-readable media for solving large, sparse convex optimization problems on highly parallel hardware. A computing apparatus receives an initial point and iteratively updates an optimization point via a first subsystem that constructs and updates a Karush-Kuhn-Tucker (KKT) system and applies small steps in an improving direction. In parallel, a second subsystem incrementally solves the updated KKT system, while a third subsystem optimizes parameters such as preconditioners and matrix permutations. Outputs are exchanged among subsystems during each iteration to enable continuous refinement. A small-step criterion, enforced via line search or trust-region procedures, ensures convergence. Execution leverages GPUs or TPUs to partition and process KKT systems concurrently, dynamically adjusting step size based on convergence indicators.

The invention relates to the technical field of integrated circuits, and provides a nonlinear circuit transient simulation method and related equipment, and the method comprises the steps: obtaining a circuit state of a nonlinear target simulation circuit at a current simulation moment; constructing a second-order exponential integral model of the target simulation circuit based on the circuit state of the target simulation circuit at the current simulation moment; constructing an iteration solver based on the second-order exponential integral model, and constructing a structure precondition device based on the target simulation circuit; performing iterative solution according to the iterative solver and the structure precondition device to obtain a predicted circuit state of the target simulation circuit at the next simulation moment of the current simulation moment; and simulating the target simulation circuit based on the predicted circuit state. According to the method, the transient simulation precision and efficiency of the nonlinear circuit can be improved.

The invention provides an OpenFOAM simulation method based on GPU acceleration, electronic equipment and a readable medium, and relates to the technical field of computational fluid dynamics. The OpenFOAM simulation method comprises the steps that OpenFOAM grid information is read, an LDU matrix is generated, and a special function is written to convert the LDU matrix into an ELLPACK-R format; performing dynamic grid division on the initial grid according to the calculation load of each GPU, performing compression processing on the matrix and vector data in the ELLPACK-R format, and transmitting the compressed data from the CPU memory to the corresponding GPU memory; writing an optimized CUDA kernel function, and executing matrix vector multiplication in an ELLPACK-R format on a GPU (Graphics Processing Unit); a PCG solver is initialized on the GPU, the matrix in the ELLPACK-R format is preprocessed through an AMG-ILU self-adaptive preprocessor, and then iterative calculation of the PCG solver is executed; decompressing an iterative calculation result, transmitting the decompressed iterative calculation result back to a CPU memory, and converting a decompressed solution vector into an LDU matrix format which can be identified by OpenFOAM; and operating OpenFOAM simulation, and dynamically adjusting a dynamic grid division strategy and parameters of a PCG solver according to a simulation result.

The application discloses an intelligent selection method of CFD sparse linear system iteration strategy and preconditioner based on structure-aware graph embedding, and relates to the technical field of computers, comprising: in the process of executing a target simulation task based on a CFD simulation software, collecting data by using a residual-driven sampling mechanism to obtain a physically consistent data set, performing graph modeling based on the physically consistent data set, and determining a graph embedding vector by using a graph modeling result and a structure-aware graph embedding model; determining a combination selection result of the CFD sparse linear system iteration strategy and the preconditioner based on the graph embedding vector and a decoupled integrated decision recommendation system; and completing a CFD numerical simulation operation corresponding to the target simulation task based on the combination selection result and the CFD simulation software. The application effectively solves the problems in the prior art, thereby improving the efficiency of CFD numerical simulation and reducing the collapse risk in large-scale simulation.

The invention discloses an ill-conditioned matrix SVD decomposition preprocessing method and device based on deep learning, and a medium, and belongs to the technical field of numerical algebra and deep learning. The method comprises the following steps: constructing an iterative deep neural network learning framework, extracting matrix features by using a convolutional layer, and constructing an orthogonal matrix through a House holder reflection decomposition method; training the network by using a mixed loss function, forcing the network to output an approximate diagonal matrix by punishing off-diagonal elements, and performing multiple rounds of iterative optimization by taking a learning result as the input of a new round of training; a precondition is constructed based on a matrix obtained through training, an original ill-conditioned linear equation set is preprocessed, and a preconditioned equation set is solved through an iterative algorithm. The method can be adapted to an ill-conditioned general matrix without a special structure, effectively solves the problems of insufficient approximation precision and poor generalization ability of a traditional method, and remarkably improves the numerical stability, convergence efficiency and calculation precision of high-dimensional ill-conditioned matrix solution.