A Sparse Grid Discretization with Variable Coefficient in High Dimensions
For researchers solving high-dimensional elliptic PDEs, this provides an efficient sparse grid method with pre-wavelets, though the approach is incremental as it combines existing techniques.
The paper presents a sparse grid discretization using pre-wavelets for solving elliptic PDEs with variable coefficients in high dimensions (d=2,3,6+). The method achieves convergence consistent with finite element approximation and maintains a condition number below 10 with diagonal preconditioning.
We present a Ritz-Galerkin discretization on sparse grids using pre-wavelets, which allows to solve elliptic differential equations with variable coefficients for dimension $d=2,3$ and higher dimensions $d>3$. The method applies multilinear finite elements. We introduce an efficient algorithm for matrix vector multiplication using a Ritz-Galerkin discretization and semi-orthogonality. This algorithm is based on standard 1-dimensional restrictions and prolongations, a simple pre-wavelet stencil, and the classical operator dependent stencil for multilinear finite elements. Numerical simulation results are presented for a 3-dimensional problem on a curvilinear bounded domain and for a 6-dimensional problem with variable coefficients. Simulation results show a convergence of the discretization according to the approximation properties of the finite element space. The condition number of the stiffness matrix can be bounded below $10$ using a standard diagonal preconditioner.