<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-22T06:11:35Z</responseDate><request verb="GetRecord" identifier="oai:ruor.uottawa.ca:10393/34327" metadataPrefix="oai_dc">https://ruor.uottawa.ca/server/oai/request</request><GetRecord><record><header><identifier>oai:ruor.uottawa.ca:10393/34327</identifier><datestamp>2024-02-23T08:57:08Z</datestamp><setSpec>com_10393_242</setSpec><setSpec>col_10393_11105</setSpec></header><metadata><oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
   <dc:title>Spectral Solution Method for Distributed Delay Stochastic Differential Equations</dc:title>
   <dc:creator>René, Alexandre</dc:creator>
   <dc:contributor>Longtin, André</dc:contributor>
   <dc:subject>stochastic differential equations</dc:subject>
   <dc:subject>distributed delay differential equations</dc:subject>
   <dc:subject>biorthogonal decomposition</dc:subject>
   <dc:description>Stochastic delay differential equations naturally arise in models of complex natural phenomena, yet continue to resist efforts to find analytical solutions to them: general solutions are limited to linear systems with additive noise and a single delayed term. In this work we solve the case of distributed delays in linear systems with additive noise. Key to our solution is the development of a consistent interpretation for integrals over stochastic variables, obtained by means of a virtual discretization procedure. This procedure makes no assumption on the form of noise, and would likely be useful for a wider variety of cases than those we have considered. We show how it can be used to map the distributed delay equation to a known multivariate system, and obtain expressions for the system&amp;apos;s time-dependent mean and autocovariance. These are in the form of series over the system&amp;apos;s natural modes and completely define the solution.  — An interpretation of the system as an amplitude process is explored. We show that for a wide range of realistic parameters, dynamics are dominated by only a few modes, implying that most of the observed behaviour of stochastic delayed equations is constrained to a low-dimensional subspace.  — The expression for the autocovariance is given particular attention. A recurring problem for stochastic delay equations is the description of their temporal structure. We show that the series expression for the autocovariance does converge over a meaningful range of time lags, and therefore provides a means of describing this temporal structure.</dc:description>
   <dc:date>2016-03-03T12:49:13Z</dc:date>
   <dc:date>2016-03-03T12:49:13Z</dc:date>
   <dc:date>2016</dc:date>
   <dc:type>Thesis</dc:type>
   <dc:identifier>http://hdl.handle.net/10393/34327</dc:identifier>
   <dc:identifier>http://dx.doi.org/10.20381/ruor-5172</dc:identifier>
   <dc:language>en</dc:language>
   <dc:format>application/pdf</dc:format>
   <dc:publisher>Université d&amp;apos;Ottawa / University of Ottawa</dc:publisher>
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