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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Training of Physical Neural Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2406.03372/</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/2406.03372/feed.xml" rel="self" type="application/rss+xml" /><item><title>Training of Physical Neural Networks — mentioned by SaberTail</title><link>https://news.ycombinator.com/item?id=47506968</link><guid isPermaLink="false">urn:hackerpapers:mention:2406.03372:47506968</guid><pubDate>Tue, 24 Mar 2026 18:22:59 GMT</pubDate><description>&lt;p&gt;The term I&amp;#x27;ve heard for this sort of thing is &amp;quot;Physical Neural Networks&amp;quot; or &amp;quot;PNN&amp;quot;s. My impression is that one of the big things holding them back is that because we can&amp;#x27;t manufacture components to perfect tolerances, you can&amp;#x27;t train a single model and reuse it like you can with digital logic. Even if you can get close, every single circuit needs some amount of tuning. And we haven&amp;#x27;t worked out great ways to train them. There&amp;#x27;s a lot of research going on in this space though, because yeah, nature can solve certain mathematical problems more efficiently than digital systems. There&amp;#x27;s a decent…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2406.03372/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>physics.app-ph</category></item><item><title>Training of Physical Neural Networks — HN submission</title><link>https://news.ycombinator.com/item?id=40926515</link><guid isPermaLink="false">urn:hackerpapers:submission:2406.03372:40926515</guid><pubDate>Wed, 10 Jul 2024 13:13:51 GMT</pubDate><description>&lt;p&gt;Training of Physical Neural Networks&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;142 points; 46 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2406.03372/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>physics.app-ph</category></item></channel></rss>
