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J. Probab.</a> <a href="/?q=in%3A523479" title="Articles in this Issue">30, Paper No. 23, 50 p. (2025)</a>. </div> <div class="abstract">Summary: We study a class of McKean-Vlasov Stochastic Differential Equations (MV-SDEs) with drifts and diffusions having super-linear growth in measure and space &ndash; the maps have general polynomial form but also satisfy a certain monotonicity condition. The combination of the drift&rsquo;s super-linear growth in measure (by way of a convolution) and the super-linear growth in space and measure of the diffusion coefficient requires novel technical elements in order to obtain the main results. We establish wellposedness, propagation of chaos (PoC), and under further assumptions on the model parameters, we show an exponential ergodicity property alongside the existence of an invariant distribution. No differentiability or non-degeneracy conditions are required.<br class="zbmathjax-paragraph">Further, we present a particle system based Euler-type split-step scheme (SSM) for the simulation of this type of MV-SDEs. The scheme attains, in stepsize, the strong error rate \(1 / 2\) in the non-path-space root-mean-square error metric and we demonstrate the property of mean-square contraction. Our results are illustrated by numerical examples including: estimation of PoC rates across dimensions, preservation of periodic phase-space, and the observation that taming appears to be not a suitable method unless strong dissipativity is present.</div> <div class="clear"></div> <div class="classification"> <h3>MSC:</h3> <table><tr> <td> <a class="mono" href="/classification/?q=cc%3A60H10" title="MSC2020">60H10</a> </td> <td class="space"> Stochastic ordinary differential equations (aspects of stochastic analysis) </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A60H35" title="MSC2020">60H35</a> </td> <td class="space"> Computational methods for stochastic equations (aspects of stochastic analysis) </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A65C30" title="MSC2020">65C30</a> </td> <td class="space"> Numerical solutions to stochastic differential and integral equations </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A65C35" title="MSC2020">65C35</a> </td> <td class="space"> Stochastic particle methods </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A65C05" title="MSC2020">65C05</a> </td> <td class="space"> Monte Carlo methods </td> </tr></table> </div><div class="keywords"> <h3>Keywords:</h3><a href="/?q=ut%3AMcKean-Vlasov+stochastic+differential+equations">McKean-Vlasov stochastic differential equations</a>; <a href="/?q=ut%3Aergodicity">ergodicity</a>; <a href="/?q=ut%3Awell-posedness">well-posedness</a>; <a href="/?q=ut%3Ainteracting+particle+systems">interacting particle systems</a>; <a href="/?q=ut%3Asplit-step+methods">split-step methods</a></div> <!-- Modal used to show zbmath metadata in different output formats--> <div class="modal fade" id="metadataModal" tabindex="-1" role="dialog" aria-labelledby="myModalLabel"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">&times;</span></button> <h4 class="modal-title" id="myModalLabel">Cite</h4> </div> <div class="modal-body"> <div class="form-group"> <label for="select-output" class="control-label">Format</label> <select id="select-output" class="form-control" aria-label="Select Metadata format"></select> </div> <div class="form-group"> <label for="metadataText" class="control-label">Result</label> <textarea class="form-control" id="metadataText" rows="10" style="min-width: 100%;max-width: 100%"></textarea> </div> <div id="metadata-alert" class="alert alert-danger" role="alert" style="display: none;"> <!-- alert for connection errors etc --> </div> </div> <div class="modal-footer"> <button type="button" class="btn btn-primary" onclick="copyMetadata()">Copy to clipboard</button> <button type="button" class="btn btn-default" data-dismiss="modal">Close</button> </div> </div> </div> </div> <div class="functions clearfix"> <div class="function"> <!-- Button trigger metadata modal --> <a type="button" class="btn btn-default btn-xs pdf" data-toggle="modal" data-target="#metadataModal" data-itemtype="Zbl" data-itemname="Zbl 08011066" data-ciurl="/ci/08011066" data-biburl="/bibtex/08011066.bib" data-amsurl="/amsrefs/08011066.bib" data-xmlurl="/xml/08011066.xml" > Cite </a> <a class="btn btn-default btn-xs pdf" data-container="body" type="button" href="/pdf/08011066.pdf" title="Zbl 08011066 as PDF">Review PDF</a> </div> <div class="fulltexts"> <span class="fulltext">Full Text:</span> <a class="btn btn-default btn-xs" type="button" href="https://doi.org/10.1214/25-EJP1278" aria-label="DOI for “Wellposedness, exponential ergodicity and numerical approximation of fully super-linear McKean-Vlasov SDEs and associated particle systems”" title="10.1214/25-EJP1278">DOI</a> <a class="btn btn-default btn-xs" type="button" href="https://arxiv.org/abs/2302.05133"title="Note: arXiv document may differ from published version">arXiv</a> </div> <div class="sfx" style="float: right;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" title="Open Access License" class="cc-license-link no-new-tab-icon"> <img src="https://static.zbmath.org/contrib/img/cc/svg/icons/cc.svg" alt="Creative Commons CC license icon" class="cc-license-icon"> <img src="https://static.zbmath.org/contrib/img/cc/svg/icons/by.svg" alt="Creative Commons BY license icon" class="cc-license-icon"> </a> </div> </div> <div class="references"> <h3>References:</h3> <table><tr> <td>[1]</td> <td class="space">Markus Ableidinger, Evelyn Buckwar, and Harald Hinterleitner, A stochastic version of the Jansen and Rit neural mass model: Analysis and numerics, J. 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