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Department of Mathematics, University of ArizonaIssue Date
2023-10-13Keywords
35K5735K58
60J80
branching Brownian motion
interacting particle systems
reaction-diffusion equations
semilinear equations
voting models
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IOP PublishingCitation
An, J., Henderson, C., & Ryzhik, L. (2023). Voting models and semilinear parabolic equations. Nonlinearity, 36(11), 6104.Journal
NonlinearityRights
© 2023 IOP Publishing Ltd & London Mathematical Society. Original Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
We present probabilistic interpretations of solutions to semi-linear parabolic equations with polynomial nonlinearities in terms of the voting models on the genealogical trees of branching Brownian motion (BBM). These extend McKean’s connection between the Fisher-KPP equation and BBM (McKean 1975 Commun. Pure Appl. Math. 28 323-31). In particular, we present ‘random outcome’ and ‘random threshold’ voting models that yield any polynomial nonlinearity f satisfying f ( 0 ) = f ( 1 ) = 0 and a ‘recursive up the tree’ model that allows to go beyond this restriction on f. We compute several examples of particular interest; for example, we obtain a curious interpretation of the heat equation in terms of a nontrivial voting model and a ‘group-based’ voting rule that leads to a probabilistic view of the pushed-pulled transition for a class of nonlinearities introduced by Ebert and van Saarloos.Note
Open access articleISSN
0951-7715EISSN
1361-6544Version
Final published versionSponsors
Office of Naval Research Globalae974a485f413a2113503eed53cd6c53
10.1088/1361-6544/ad001c
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Except where otherwise noted, this item's license is described as © 2023 IOP Publishing Ltd & London Mathematical Society. Original Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.