Patent ID: 11914671
Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 17:
18. A computer program product for uncertainty quantification analysis with two dimensional random fields, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor in a modeling system to cause the processor to:
provide a covariance matrix of a set of parameters with a covariance function describing an occurring real world phenomenon;
generate a random field based on the covariance matrix including a controlled rank reduction to produce a reduced-rank random field dataset provided as a data structure with data compression, wherein generating the random field applies rank reduction of a block circulant with circulant blocks (BCCB) representation of the covariance matrix obtained from the covariance matrix using existing symmetry of eigenvalues to eliminate redundant computations;
output the reduced-rank random field dataset for use in a user-provided model of the modeling system for uncertainty quantification analysis and simulation of the real world phenomenon,
for applying the rank reduction to the BCCB representation of the covariance, the processor caused to reduce the BCCB representation of the covariance matrix entries by:
appending extracted reduced entries of a first column and row of the BCCB representation of the covariance matrix to an array, the extracted reduced entries including a portion of the first row of the BCCB representation of the covariance matrix and a portion of the first column of the matrix;
reduction vectorizing a remaining inner section of the BCCB representation of the covariance matrix and including the reduction vectorized remaining inner section of the BCCB representation of the covariance matrix to the array, wherein the reduction vectorizing linearizes the remaining inner section of the BCCB representation of the covariance matrix, wherein the appending and reduction vectorizing linearizes a dataset of the BCCB representation of the covariance matrix in the array for a subsequent sort operation;
sorting the array;
removing redundant conjugate pairs according to a user-specified threshold; and

reconstruct the BCCB representation of the covariance matrix by adding plane reflections of the first row and column and performing point symmetry on a central part,
the processor further caused to apply a random vector to the reduced and reconstructed matrix in generating the reduced-rank random field dataset, and
input the reduced-rank random field dataset into a user-provided model and using the model, run an uncertainty quantification (UQ) analysis,
wherein the data compression reduces data storage usage and at least the rank reduction accelerates the speed of the processor performing the uncertainty quantification analysis and simulation of the real world phenomenon.