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

Claim 9:
10. A system for uncertainty quantification analysis with two dimensional random fields, comprising:
a processor; and a memory configured to provide computer program instructions to the processor;
the processor configured to run parallel processing threads 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 applying controlled rank reduction to produce a reduced-rank random field dataset 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; and
output the reduced-rank random field dataset for use in a user-provided model for uncertainty quantification analysis and simulation of the real world phenomenon,
the processor is configured 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 matrix and a portion of the first column of the BCCB representation of the covariance 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 in 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 to the array for a subsequent sort operation;
sorting the array;
removing redundant conjugate pairs according to a user-specified threshold; and
reconstructing 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 configured to apply a random vector to the reduced and reconstructed BCCB representation of the covariance 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.