7 citations · 13 across the 9 of their papers we have counts for
9 papers
Reversible Deep Learning for 13C NMR in Chemoinformatics: On Structures and Spectra
Stefan Kuhn, Vandana Dwarka, Przemyslaw Karol Grenda +1
We introduce a reversible deep learning model for 13C NMR that uses a single conditional invertible neural network for both directions between molecular structures and spectra. The…
Matrix-Free Parallel Scalable Multilevel Deflation Preconditioning for Heterogeneous Time-Harmonic Wave Problems
Jinqiang Chen, Vandana Dwarka, Cornelis Vuik
We present a matrix-free parallel scalable multilevel deflation preconditioned method for heterogeneous time-harmonic wave problems. Building on the higher-order deflation precondi…
Coarse Spaces Based on Higher-Order Interpolation for Schwarz Preconditioners for Helmholtz Problems
Erik Sieburgh, Alexander Heinlein, Vandana Dwarka +1
The development of scalable and wavenumber-robust iterative solvers for Helmholtz problems is challenging but also relevant for various application fields. In this work, two-level…
Towards Robust Solvers for Nuclear Fusion Simulations Using JOREK: A Numerical Analysis Perspective
Alex Quinlan, Vandana Dwarka, Ihor Holod +1
One of the most well-established codes for modeling non-linear Magnetohydrodynamics (MHD) for tokamak reactors is JOREK, which solves these equations with a Bézier surface based fi…
Stand-alone Multigrid for Helmholtz Revisited: Towards Convergence Using Standard Components
Vandana Dwarka, Cornelis Vuik
Getting standard multigrid to work efficiently for the high-frequency Helmholtz equation has been an open problem in applied mathematics for years. Much effort has been dedicated t…
A matrix-free parallel two-level deflation preconditioner for the two-dimensional Helmholtz problems
Jinqiang Chen, Vandana Dwarka, Cornelis Vuik
We propose a matrix-free parallel two-level-deflation preconditioner combined with the Complex Shifted Laplacian preconditioner(CSLP) for the two-dimensional Helmholtz problems. Th…