2014
DOI: 10.1103/physreve.90.043307
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Exhibiting cross-diffusion-induced patterns for reaction-diffusion systems on evolving domains and surfaces

Abstract: Article (Published Version) http://sro.sussex.ac.uk Madzvamuse, A and Barreira, R (2014) Exhibiting cross-diffusion-induced patterns for reactiondiffusion systems on evolving domains and surfaces. Physical Review E, 90. 043307-1-043307-14. ISSN 1539-3755 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/51334/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this… Show more

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Cited by 24 publications
(28 citation statements)
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“…For k << 1 (in particular when k gets closer to zero), the Coupled bulk-surface reaction-diffusion systems parameter spaces become almost identical and these represent parameter spaces in the absence of domain growth. As k becomes larger and larger, the parameter spaces become more distinct and larger in size (including the circular subregions), reinforcing earlier results for standard reaction-diffusion systems on growing domains [21,23,25,27]. The disconnected parameter spaces emerge due to the increase in the growth rate k as well as due to the dimension m.…”
Section: Domain-induced Parameter Spacessupporting
confidence: 55%
See 1 more Smart Citation
“…For k << 1 (in particular when k gets closer to zero), the Coupled bulk-surface reaction-diffusion systems parameter spaces become almost identical and these represent parameter spaces in the absence of domain growth. As k becomes larger and larger, the parameter spaces become more distinct and larger in size (including the circular subregions), reinforcing earlier results for standard reaction-diffusion systems on growing domains [21,23,25,27]. The disconnected parameter spaces emerge due to the increase in the growth rate k as well as due to the dimension m.…”
Section: Domain-induced Parameter Spacessupporting
confidence: 55%
“…It must be observed that for the fractional-step θ method above, the computational mesh is continuously evolving according to the exponential growth and all matrices are assembled at different discretised surfaces depending on the time-level n. The number of degrees of freedom and the mesh connectivity remains constant throughout domain growth. For further details on the implementation of the evolving (bulk) surface finite element method we refer the interested reader to [2,6,7,23].…”
Section: Time Discretisationmentioning
confidence: 99%
“…where the constant above is independent of t. ii. Let L(u) be (26), let f ∈ L 2 ( (t)) and let u ∈ H 1 ( (t)) be a weak solution of (27). Then u is a strong solution of (27), that is, u solves (27) almost everywhere and there exists a constant c > 0 independent of t and u such that ||u|| H 2 ( (t)) ≤ c(||u|| L 2 ( (t)) + ||f || L 2 ( (t)) ).…”
Section: Appendix: a Priori Estimatesmentioning
confidence: 99%
“…A great number of real-life phenomena are modeled by nonlinear parabolic problems on evolving surfaces. Apart from general quasilinear problems on moving surfaces, see for example, 3.5 in [15], more specific applications are the nonlinear models: diffusion induced grain boundary motion [16][17][18][19][20]; Allen-Cahn and Cahn-Hilliard equations on evolving surfaces [1,[21][22][23][24]; modeling solid tumor growth [20,25]; pattern formation modeled by reaction-diffusion equations [26,27]; image processing [28]; Ginzburg-Landau model for superconductivity [29].…”
Section: Introductionmentioning
confidence: 99%
“…In many applications the spatial domain is a curved surface rather than a flat domain, which may be time-dependent. Among the applications of RDSs on surfaces we mention brain growth [26], cell migration [3], chemotaxis [12], developmental biology [28], electrodeposition [24] and phase field modeling [42]. The growing interest toward PDEs on evolving surfaces has stimulated the development of several numerical methods for such problems, among which we mention (but not limited to) embedding methods [2], kernel methods [18], implicit boundary integral methods [5,35], surface finite element methods (SFEM) [10] and some of their recent variations and extensions [13,16,17,20,23,40].…”
Section: Introductionmentioning
confidence: 99%