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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data-Driven Linearization of Dynamical Systems · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2407.08177/</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/2407.08177/feed.xml" rel="self" type="application/rss+xml" /><item><title>Data-Driven Linearization of Dynamical Systems — mentioned by nihzm</title><link>https://news.ycombinator.com/item?id=46558066</link><guid isPermaLink="false">urn:hackerpapers:mention:2407.08177:46558066</guid><pubDate>Fri, 09 Jan 2026 19:28:28 GMT</pubDate><description>&lt;p&gt;&amp;gt; Sometimes models that seem nonlinear turns linear if those nonlinearities are pushed into the basis functions, so one can still hope. That idea was pushed to its limit by the Koopman operator theory. The argument sounds quite good at first, but unfortunately it can’t really work for all cases in its current formulation [1]. [1]: https://arxiv.org/abs/2407.08177&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2407.08177/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>math.DS</category></item></channel></rss>
