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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Functional Tensor Decompositions for Physics-Informed Neural Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2408.13101/</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/2408.13101/feed.xml" rel="self" type="application/rss+xml" /><item><title>Functional Tensor Decompositions for Physics-Informed Neural Networks — mentioned by AxesPushPatty</title><link>https://news.ycombinator.com/item?id=42794423</link><guid isPermaLink="false">urn:hackerpapers:mention:2408.13101:42794423</guid><pubDate>Wed, 22 Jan 2025 16:21:35 GMT</pubDate><description>&lt;p&gt;Another approach has been where separate physics-informed neural networks learned the tensor product. They reformulated the initial optimization problem to be structured as tensors. I assume that tensor products could be another factor in improving the actual computations. https://arxiv.org/abs/2408.13101&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2408.13101/"&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>
