Karolina Kropielnicka
University of Gdańsk
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Featured researches published by Karolina Kropielnicka.
Foundations of Computational Mathematics | 2014
Philipp Bader; Arieh Iserles; Karolina Kropielnicka; Pranav Singh
The computation of the semiclassical Schrödinger equation presents major challenges because of the presence of a small parameter. Assuming periodic boundary conditions, the standard approach consists of semi-discretisation with a spectral method, followed by an exponential splitting. In this paper we sketch an alternative strategy. Our analysis commences with the investigation of the free Lie algebra generated by differentiation and by multiplication with the interaction potential: it turns out that this algebra possesses a structure which renders it amenable to a very effective form of asymptotic splitting: exponential splitting where consecutive terms are scaled by increasing powers of the small parameter. This leads to methods which attain high spatial and temporal accuracy and whose cost scales as
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science | 2016
Philipp Bader; Arieh Iserles; Karolina Kropielnicka; Pranav Singh
Numerical Analysis and Applications | 2011
Z. Kamont; Karolina Kropielnicka
{\mathcal {O}}\!\left( M\log M\right)
Applicable Analysis | 2009
Z. Kamont; Karolina Kropielnicka
Computer Physics Communications | 2019
Arieh Iserles; Karolina Kropielnicka; Pranav Singh
OMlogM, where
Computational Methods in Applied Mathematics Comput | 2007
Karolina Kropielnicka
Mathematical Inequalities & Applications | 2005
Z. Kamont; Karolina Kropielnicka
M
Archive | 2007
Karolina Kropielnicka
Annales Polonici Mathematici | 2007
Karolina Kropielnicka
M is the number of degrees of freedom in the discretisation.
Journal of Mathematical Inequalities | 2008
Z. Kamont; Karolina Kropielnicka
We build efficient and unitary (hence stable) methods for the solution of the linear time-dependent Schrödinger equation with explicitly time-dependent potentials in a semiclassical regime. The Magnus–Zassenhaus schemes presented here are based on a combination of the Zassenhaus decomposition (Bader et al. 2014 Found. Comput. Math. 14, 689–720. (doi:10.1007/s10208-013-9182-8)) with the Magnus expansion of the time-dependent Hamiltonian. We conclude with numerical experiments.