<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Variational Autoencoder on Andrea Nóvoa</title><link>https://andreanovoa.github.io/tags/variational-autoencoder/</link><description>Recent content in Variational Autoencoder on Andrea Nóvoa</description><generator>Hugo -- 0.147.2</generator><language>en</language><lastBuildDate>Mon, 17 Aug 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://andreanovoa.github.io/tags/variational-autoencoder/index.xml" rel="self" type="application/rss+xml"/><item><title>Efficient adaptation of ROMs for unsteady flows using data assimilation</title><link>https://andreanovoa.github.io/papers/2608/</link><pubDate>Mon, 17 Aug 2026 00:00:00 +0000</pubDate><guid>https://andreanovoa.github.io/papers/2608/</guid><description>This paper proposes an efficient retraining strategy for parameterized reduced-order models, which adapts a variational autoencoder and transformer architecture to out-of-sample flow regimes from sparse observations.</description></item></channel></rss>