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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>NeuralSim: Augmenting Differentiable Simulators with Neural Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2011.04217/</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/2011.04217/feed.xml" rel="self" type="application/rss+xml" /><item><title>NeuralSim: Augmenting Differentiable Simulators with Neural Networks — mentioned by radarsat1</title><link>https://news.ycombinator.com/item?id=32329783</link><guid isPermaLink="false">urn:hackerpapers:mention:2011.04217:32329783</guid><pubDate>Wed, 03 Aug 2022 10:15:21 GMT</pubDate><description>&lt;p&gt;This reminded me a great deal of a paper I read like 10 years ago, which I just looked up because I wanted to see if it was cited here, &amp;quot;Distilling Free-Form Natural Laws from Experimental Data&amp;quot; [0]. Lo and behold it is not only cited, but is written by the same authors. So the novelty here, as discussed in the paper [1], is that they are doing something similar but from video instead of from sensor streams. Which is quite interesting, as it opens up the available information to systems that are hard to &amp;#x27;sense&amp;#x27; apart from pointing a camera at them, like the lava lamp example. I did see a…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2011.04217/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.RO</category></item></channel></rss>
