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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Differentiable Programming for Differential Equations: A Review · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2406.09699/</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/2406.09699/feed.xml" rel="self" type="application/rss+xml" /><item><title>Differentiable Programming for Differential Equations: A Review — mentioned by ChrisRackauckas</title><link>https://news.ycombinator.com/item?id=45291554</link><guid isPermaLink="false">urn:hackerpapers:mention:2406.09699:45291554</guid><pubDate>Thu, 18 Sep 2025 16:22:04 GMT</pubDate><description>&lt;p&gt;Interesting to see this here! This example is one of the ones mentioned in the appendix of https://arxiv.org/abs/2406.09699, specifically &amp;quot;4.1.2.4 When AD is algorithmically correct but numerically wrong&amp;quot;. If people want a tl;dr, the main idea is you can construct an ODE where the forward pass is trivial, i.e. the ODE solution going forwards is exact, but its derivative is &amp;quot;hard&amp;quot;. An easy way to do this is to make it so you have for example `x&amp;#x27; = x - y, y&amp;#x27; = y - x`, with initial conditions x(0)=y(0). If you start with things being the same value, then the solution to the ODE is constant since…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2406.09699/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>math.NA</category></item></channel></rss>
