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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Probabilistic solution of chaotic dynamical system inverse problems using Bayesian Artificial Neural Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2005.13028/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2005.13028/feed.xml" rel="self" type="application/rss+xml" /><item><title>Probabilistic solution of chaotic dynamical system inverse problems using Bayesian Artificial Neural Networks — mentioned by dgr582systems</title><link>https://news.ycombinator.com/item?id=24969709</link><guid isPermaLink="false">urn:hackerpapers:mention:2005.13028:24969709</guid><pubDate>Mon, 02 Nov 2020 16:18:39 GMT</pubDate><description>&lt;p&gt;People are working on it (disclosure, that includes me - https://arxiv.org/abs/2005.13028) There is a special interest group at The Alan Turing Institute in the UK dedicated to working on problems of this sort. There are a number of challenges that vary depending on both the system and the forcing.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2005.13028/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
