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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data-driven discovery of intrinsic dynamics · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2108.05928/</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/2108.05928/feed.xml" rel="self" type="application/rss+xml" /><item><title>Data-driven discovery of intrinsic dynamics — mentioned by PaulHoule</title><link>https://news.ycombinator.com/item?id=33911334</link><guid isPermaLink="false">urn:hackerpapers:mention:2108.05928:33911334</guid><pubDate>Thu, 08 Dec 2022 18:32:28 GMT</pubDate><description>&lt;p&gt;Note learning the manifolds that ‘strange attractors’ live on was big in the 1980s but the breakthroughs in discovering structures in high dimension spaces from neural networks are paying off for dynamic systems. Open access preprint: https://arxiv.org/pdf/2108.05928.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2108.05928/"&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>
