<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Neural ODEs on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/neural-odes/</link><description>Recent content in Neural ODEs on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 15 Apr 2024 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/neural-odes/index.xml" rel="self" type="application/rss+xml"/><item><title>Ordinary Differential Equations (18): Frontiers and Series Finale</title><link>https://www.chenk.top/en/ode/18-advanced-topics-summary/</link><pubDate>Mon, 15 Apr 2024 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/ode/18-advanced-topics-summary/</guid><description>&lt;p>&lt;strong>The journey ends here.&lt;/strong> Eighteen chapters ago we picked up a falling apple. Today we&amp;rsquo;re going to finish in the same vein in which we began — by treating ODEs as the &lt;em>universal language of change&lt;/em> — but standing on a much taller mountain.&lt;/p>
&lt;p>This chapter does three things. First, it surveys four active research frontiers that are reshaping how we &lt;em>model&lt;/em> dynamical systems: Neural ODEs, delay equations, stochastic differential equations, and fractional calculus. Second, it reviews the entire series with a problem-solving flowchart and a chapter-by-chapter map. Third, it draws explicit connections from the classical theory you have just mastered to modern machine learning — the place where ODEs are most alive in 2025.&lt;/p></description></item></channel></rss>