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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learning to Simulate Complex Physics with Graph Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2002.09405/</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/2002.09405/feed.xml" rel="self" type="application/rss+xml" /><item><title>Learning to Simulate Complex Physics with Graph Networks — mentioned by nerpderp82</title><link>https://news.ycombinator.com/item?id=35010919</link><guid isPermaLink="false">urn:hackerpapers:mention:2002.09405:35010919</guid><pubDate>Fri, 03 Mar 2023 15:34:41 GMT</pubDate><description>&lt;p&gt;HashLife seems like a scale free memoizer, https://en.wikipedia.org/wiki/Hashlife How Well Can DeepMind&amp;#x27;s AI Learn Physics? https://www.youtube.com/watch?v=2Bw5f4vYL98 https://arxiv.org/abs/2002.09405 https://sites.google.com/corp/view/learning-to-simulate/home Discovering Symbolic Models from Deep Learning (Physics) https://www.youtube.com/watch?v=HKJB0Bjo6tQ Scientific Machine Learning: Physics-Informed Neural Networks with Craig Gin https://www.youtube.com/watch?v=RTPo6KgpvBA Steve Brunton&amp;#x27;s channel is even more mind blowing than Two Minute Papers, https://www.youtube.com/@Eigensteve Not…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2002.09405/"&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>
