<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
<channel>
<title>Surrogate Models: Biomedical flows</title>
<link>https://surrogatemodels.com/</link>
<atom:link href="https://surrogatemodels.com/feed/biomedical-flows.xml" rel="self" type="application/rss+xml" />
<description>News, papers, datasets, and funding and deals in Physics AI for engineers: Biomedical flows.</description>
<language>en</language>
<item><title>NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework</title><link>https://surrogatemodels.com/papers/simnet</link><guid isPermaLink="true">https://surrogatemodels.com/papers/simnet</guid><pubDate>Thu, 12 Nov 2026 09:00:00 GMT</pubDate><description>A framework that trains physics-informed networks from the governing equations, with no simulation data, on geometry built from constructive solid geometry or STL. Design parameters such as fin heights are extra network inputs, so one training run covers a whole family of designs.</description><category>Aero &amp; CFD</category><category>Thermal</category><category>Biomedical flows</category></item>
</channel>
</rss>
