Patents by Inventor DENNIS GIANNACOPOULOS

DENNIS GIANNACOPOULOS has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 10394978
    Abstract: Herein provided are methods and systems for generating finite element modelling results. Finite element method (FEM) data relating to establish a FEM problem to be solved for a portion of a physical system being analyzed is received. A FEM mesh comprising at least FEM mesh node locations relating to the portion of the physical system is generated. FEM mesh values for each FEM mesh node location are automatically generated with a microprocessor. A factor graph model comprising a plurality of random variable nodes and a plurality of factor nodes is automatically generated with a microprocessor based upon the FEM mesh node locations. A set of belief propagation update rules are automatically executed upon the factor graph model using Gaussian function parametrization and the FEM mesh values. The belief propagation update rules are iteratively executed until a predetermined condition has been met.
    Type: Grant
    Filed: October 29, 2014
    Date of Patent: August 27, 2019
    Assignee: THE ROYAL INSTITUTION FOR THE ADVANCEMENT OF LEARNING / MCGILL UNIVERSITY
    Inventors: Dennis Giannacopoulos, Yousef El Kurdi, Warren Gross
  • Publication number: 20150120261
    Abstract: The computational efficiency of Finite Element Methods (FEM) on parallel architectures is typically severely limited by sparse iterative solvers. Standard iterative solvers are based on sequential steps of global algebraic operations, which limit their parallel efficiency, and prior art techniques exploit sophisticated programming techniques tailored to specific CPU architectures to improve performance. The inventors present a FEM Multigrid Gaussian Belief Propagation (FMGaBP) technique that eliminates global algebraic operations and sparse data-structures based upon reformulating the variational FEM into a probabilistic inference problem based upon graphical models. Further, the inventors present new formulations for FMGaBP, which further enhance its computation and communication complexities where the parallel features of FMGaBP are leveraged to multicore architectures.
    Type: Application
    Filed: October 29, 2014
    Publication date: April 30, 2015
    Inventors: DENNIS GIANNACOPOULOS, YOUSEF EL KURDI, WARREN GROSS