<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>FNO on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/fno/</link><description>Recent content in FNO on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 16 May 2024 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/fno/index.xml" rel="self" type="application/rss+xml"/><item><title>PDE and ML (2): Neural Operator Theory</title><link>https://www.chenk.top/en/pde-ml/02-neural-operator-theory/</link><pubDate>Thu, 16 May 2024 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/pde-ml/02-neural-operator-theory/</guid><description>&lt;p>A classical PDE solver — finite difference, finite element, spectral — is a function: feed it one initial condition and one set of coefficients, get back one solution. A PINN is the same kind of object dressed in neural-network clothes: each new initial condition demands a fresh round of training. Switch the inflow velocity on a wing or move a single sensor reading in a forecast and you reset the clock.&lt;/p></description></item></channel></rss>