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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Neural Operator: Graph Kernel Network for Partial Differential Equations · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2003.03485/</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/2003.03485/feed.xml" rel="self" type="application/rss+xml" /><item><title>Neural Operator: Graph Kernel Network for Partial Differential Equations — mentioned by w-m</title><link>https://news.ycombinator.com/item?id=36850924</link><guid isPermaLink="false">urn:hackerpapers:mention:2003.03485:36850924</guid><pubDate>Mon, 24 Jul 2023 16:52:49 GMT</pubDate><description>&lt;p&gt;Indeed, this seems closely related, thanks for the pointer! Unfortunately I&amp;#x27;m not deep enough into the topic to understand what their contribution to the theory part of it is. (they have some Supplementary Material in [INN Supp]). In the discussion of the Integral Neural Networks (INN) paper, there&amp;#x27;s this paragraph about an operator learning publication: &amp;quot;In [24] the authors proposed deep neural networks with layers defined as functional operators. Such networks are designed for learning PDE solution operators, and its layers are continuously parameterized by MLPs only along the kernel…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2003.03485/"&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>
