TY - JOUR
T1 - Adaptive Finite Element Method Assisted by Stochastic Simulation of Chemical Systems
AU - Cotter, Simon L.
AU - Vejchodský, Tomáš
AU - Erban, Radek
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUK-C1-013-04
Acknowledgements: Submitted to the journal's Computational Methods in Science and Engineering section May 15, 2012; accepted for publication (in revised form) December 3, 2012; published electronically January 10, 2013. This work was supported by the European Research Council under the European Community's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement 239870 and was based on work supported in part by award KUK-C1-013-04, made by King Abdullah University of Science and Technology (KAUST).School of Mathematics, University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom ([email protected]). This author's work was partially supported by a Junior Research Fellowship of St Cross College, University of Oxford.Institute of Mathematics, Czech Academy of Sciences, Zitna 25, 115 67 Praha 1, Czech Republic ([email protected]). This author's work was supported by the Grant Agency of the Academy of Sciences (project IAA100190803) and RVO 67985840.Mathematical Institute, University of Oxford, 24-29 St. Giles', Oxford, OX1 3LB, United Kingdom ([email protected]). This author's work was supported by Somerville College, University of Oxford, by a Fulford Junior Research Fellowship; Brasenose College, University of Oxford, by a Nicholas Kurti Junior Fellowship; the Royal Society for a University Research Fellowship; and the Leverhulme Trust for a Philip Leverhulme Prize. This prize money was used to support research visits of Tomas Vejchodsky in Oxford.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
PY - 2013/1
Y1 - 2013/1
N2 - Stochastic models of chemical systems are often analyzed by solving the corresponding Fokker-Planck equation, which is a drift-diffusion partial differential equation for the probability distribution function. Efficient numerical solution of the Fokker-Planck equation requires adaptive mesh refinements. In this paper, we present a mesh refinement approach which makes use of a stochastic simulation of the underlying chemical system. By observing the stochastic trajectory for a relatively short amount of time, the areas of the state space with nonnegligible probability density are identified. By refining the finite element mesh in these areas, and coarsening elsewhere, a suitable mesh is constructed and used for the computation of the stationary probability density. Numerical examples demonstrate that the presented method is competitive with existing a posteriori methods. © 2013 Society for Industrial and Applied Mathematics.
AB - Stochastic models of chemical systems are often analyzed by solving the corresponding Fokker-Planck equation, which is a drift-diffusion partial differential equation for the probability distribution function. Efficient numerical solution of the Fokker-Planck equation requires adaptive mesh refinements. In this paper, we present a mesh refinement approach which makes use of a stochastic simulation of the underlying chemical system. By observing the stochastic trajectory for a relatively short amount of time, the areas of the state space with nonnegligible probability density are identified. By refining the finite element mesh in these areas, and coarsening elsewhere, a suitable mesh is constructed and used for the computation of the stationary probability density. Numerical examples demonstrate that the presented method is competitive with existing a posteriori methods. © 2013 Society for Industrial and Applied Mathematics.
UR - http://hdl.handle.net/10754/597453
UR - http://epubs.siam.org/doi/10.1137/120877374
UR - http://www.scopus.com/inward/record.url?scp=84876205338&partnerID=8YFLogxK
U2 - 10.1137/120877374
DO - 10.1137/120877374
M3 - Article
SN - 1064-8275
VL - 35
SP - B107-B131
JO - SIAM Journal on Scientific Computing
JF - SIAM Journal on Scientific Computing
IS - 1
ER -