https://hackerpaper.kaipfister.de/p/2211.08064/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMThttps://news.ycombinator.com/item?id=42275023urn:hackerpapers:mention:2211.08064:42275023Fri, 29 Nov 2024 16:42:30 GMT<p>&quot;Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications&quot; (2024) https://arxiv.org/abs/2211.08064 : &gt; We systematically review the recent development of physics-informed machine learning from three perspectives of machine learning tasks, representation of physical prior, and methods for incorporating physical prior. We also propose several important open research problems based on the current trends in the field. We argue that encoding different forms of physical prior into model architectures, optimizers, inference algorithms, and significant domain-specific…</p><p><a href="https://hackerpaper.kaipfister.de/p/2211.08064/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.LGhttps://news.ycombinator.com/item?id=42274748urn:hackerpapers:submission:2211.08064:42274748Fri, 29 Nov 2024 16:04:38 GMT<p>Physics-Informed Machine Learning: A Survey</p><p></p><p>3 points; 1 discussion reply on HN. <a href="https://hackerpaper.kaipfister.de/p/2211.08064/">Paper and source history</a>. A submission is not an endorsement.</p>cs.LG