venkovic Goto Github PK
Name: Nicolas Venkovic
Type: User
Company: Technical University of Munich
Bio: Postdoctoral Researcher
Location: Germany
Name: Nicolas Venkovic
Type: User
Company: Technical University of Munich
Bio: Postdoctoral Researcher
Location: Germany
Python wrapped C++ code for bottom-up dynamic computation of high order gradient components of 2D linear elastic Green functions in anistropic, orthotropic, R0-orthotropic, square symmetric and isotropic media.
C++ matrix-free FFT-based Richardson solver for viscoplastic simulation on structured grids with heterogeneous and anisotropic coefficients.
Python wrapped C++ funky discretization of PDE with high order Minkowski tensors for non-convex anisotropic polycrystals based on the Hashin-Shtrikman variational principle with piecewise polynomial trial fields.
C++ code for FFT-based estimation of 2- and 3-points correlation functions of 2D signals sampled on structured grids.
C++ code to compute boundary curves, overlays and high order Minkowski tensor components of all types in 2D anisotropic Voronoi diagrams.
OpenMP Fortran code to compute volumes and connectivity of 3D weighted Voronoi diagrams associated with marked point patterns.
Julia code to enable fast and low storage matrix-vector products with covariance matrices of stationary Gaussian processes.
Julia code and Python post-processing scripts for the PhD thesis of Venkovic (2023): Galerkin P1 finite element method, geometric domain decomposition, recycled conjugate gradient algorithms and preconditioning strategies for 2D random variable coefficient Poisson equations with parallel computation of Karhunen-Loève decompositions.
Julia code to benchmark recycling Krylov subspace strategies for sequences of linear systems with multiple right-hand sides and/or sparse SPD matrices.
Julia interface to Metis graph partitioning
Iterative methods for Gaussian process regression
Enables testing and applications of deflation and preconditioning strategies to solve sequences of sampled finite element (FE) discretizations of stochastic differential equations (SDE).
Python script for deterministic FEM solves of stochastic elliptic PDEs with random coefficients.
Enables testing and applications of restarting strategies for Lanczos tridiagonalizations used in solving sequences of eigenvalue problems.
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