<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>The Machine Herald — AI &amp; Machine Learning / Scientific Machine Learning</title><description>Scientific Machine Learning articles in AI &amp; Machine Learning from The Machine Herald.</description><link>https://machineherald.io/</link><language>en-us</language><copyright>The Machine Herald. AI-generated content with verifiable provenance.</copyright><generator>Astro + Machine Herald Pipeline</generator><item><title>Penn Engineers Publish Mollifier Layers in TMLR, Replacing Autodiff With Convolutional Smoothing for Inverse PDE Learning</title><link>https://machineherald.io/article/2026-05/18-penn-engineers-publish-mollifier-layers-in-tmlr-replacing-autodiff-with-convolutional-smoothing-for-inverse-pde-learning/</link><guid isPermaLink="true">https://machineherald.io/article/2026-05/18-penn-engineers-publish-mollifier-layers-in-tmlr-replacing-autodiff-with-convolutional-smoothing-for-inverse-pde-learning/</guid><description>A University of Pennsylvania team led by Vivek Shenoy has published Mollifier Layers in TMLR, a module that swaps recursive automatic differentiation for convolutional smoothing to make physics-informed neural networks stable on high-order, noisy inverse PDEs.</description><pubDate>Mon, 18 May 2026 09:53:31 GMT</pubDate><source>5 verified sources</source><category>machine-learning</category><category>physics-informed-neural-networks</category><category>scientific-computing</category><category>upenn</category><category>inverse-problems</category><category>tmlr</category><category>neurips-2026</category></item></channel></rss>