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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2407.07218/</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/2407.07218/feed.xml" rel="self" type="application/rss+xml" /><item><title>Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations — HN submission</title><link>https://news.ycombinator.com/item?id=44660020</link><guid isPermaLink="false">urn:hackerpapers:submission:2407.07218:44660020</guid><pubDate>Wed, 23 Jul 2025 15:02:52 GMT</pubDate><description>&lt;p&gt;Weak baselines and reporting biases lead to overoptimism in ML for fluid PDEs&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;2 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2407.07218/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>math.NA</category></item><item><title>Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations — mentioned by cherryteastain</title><link>https://news.ycombinator.com/item?id=42470363</link><guid isPermaLink="false">urn:hackerpapers:mention:2407.07218:42470363</guid><pubDate>Fri, 20 Dec 2024 11:56:10 GMT</pubDate><description>&lt;p&gt;For physics sims, I&amp;#x27;d say it&amp;#x27;s useless. Imagine you discretize a cube into 1000 gridpoints in each direction, that&amp;#x27;s 1000^3 = 1 billion nodes/&amp;quot;tokens&amp;quot;. Plus you typically time-march some sort of equation so you need the solutions previous 3-5 timesteps as well so that&amp;#x27;s 3-5 billion tokens. If you are gonna do that in the first place, you may as well just use the traditional solver. Traditional solvers usually set up and solve a matrix equation like Ax=b with an iterative method like multigrid which is O(n) as opposed to transformer&amp;#x27;s O(n^2). It&amp;#x27;ll give you a much more accurate answer much…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2407.07218/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>math.NA</category></item><item><title>Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations — mentioned by al_th</title><link>https://news.ycombinator.com/item?id=42214130</link><guid isPermaLink="false">urn:hackerpapers:mention:2407.07218:42214130</guid><pubDate>Fri, 22 Nov 2024 14:34:20 GMT</pubDate><description>&lt;p&gt;Interesting work. Given, the recent noise around this paper https://arxiv.org/pdf/2407.07218 about &amp;quot;weak baselines&amp;quot; in ML x CFD work, I wonder how it resonates with this specific work.. I am not super familiar with DEM, but I know that other particle based model such as SPH benefit immensely from GPU acceleration. Does it make sense to compare with a CPU implementation ? Besides, the output of the NeuralDEM seems to be rather coarse fields, correct ? In that sense, and again I&amp;#x27;m not an expert of granular models so I might be entirely wrong, but does it make sense to compare with a method that…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2407.07218/"&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>
