<?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>Digital Twin on Jaehyeon Kim</title><link>https://jaehyeon.me/tags/digital-twin/</link><description>Recent content in Digital Twin on Jaehyeon Kim</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>Copyright © 2023-2026 Jaehyeon Kim. All Rights Reserved.</copyright><lastBuildDate>Fri, 17 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://jaehyeon.me/tags/digital-twin/index.xml" rel="self" type="application/rss+xml"/><item><title>Dynamic DES v0.11.1: A Declarative API with Postgres and Redis Connectors</title><link>https://jaehyeon.me/blog/2026-07-17-dynamic-des-declarative-connectors/</link><pubDate>Fri, 17 Jul 2026 00:00:00 +0000</pubDate><guid>https://jaehyeon.me/blog/2026-07-17-dynamic-des-declarative-connectors/</guid><description><![CDATA[<p>A while back I wrote about <a href="/blog/2026-05-25-dynamic-des-parquet-support/">Dynamic DES v0.8.1</a> and its native Data Lake integration, using one SimPy codebase for both batch training and live inference. Several releases later, the project has grown in two directions that matter for anyone building event-driven digital twins: a cleaner authoring experience, and more places to send and receive data.</p>
<p>Here is what changed on the way to <strong>v0.11.1</strong>.</p>]]></description><enclosure url="https://jaehyeon.me/blog/2026-07-17-dynamic-des-declarative-connectors/featured.png" length="212473" type="image/png"/></item><item><title>Building an Event-Driven Hybrid Digital Twin with dynamic-des</title><link>https://jaehyeon.me/blog/2026-04-28-digital-twin-dynamic-des/</link><pubDate>Wed, 29 Apr 2026 00:00:00 +0000</pubDate><guid>https://jaehyeon.me/blog/2026-04-28-digital-twin-dynamic-des/</guid><description>Asynchronous Gap In Part 1, we established that a true Hybrid Digital Twin does more than just mirror reality. It actively forecasts the future by running a simulation against live operational states.
If you have ever tried to build one of these systems from scratch, you immediately hit a fundamental architectural clash.
Standard simulation clocks (like those in traditional SimPy implementations) are logically synchronous and not designed to handle high-frequency asynchronous I/O without explicit decoupling.</description><enclosure url="https://jaehyeon.me/blog/2026-04-28-digital-twin-dynamic-des/featured.png" length="177036" type="image/png"/></item><item><title>Why Digital Twins Are Rewiring Industry 4.0</title><link>https://jaehyeon.me/blog/2026-04-23-digital-twin-industry-4-0/</link><pubDate>Wed, 22 Apr 2026 00:00:00 +0000</pubDate><guid>https://jaehyeon.me/blog/2026-04-23-digital-twin-industry-4-0/</guid><description>Beyond CAD Models There is a project by Dassault Systèmes called the Living Heart that illustrates the trajectory of this technology. Instead of relying on standard 2D scans, surgeons can pull up a high-fidelity 3D model of a patient&amp;rsquo;s heart that simulates blood flow, mechanics, and electricity based on imaging-derived reconstructions and population-based physiological calibration.
While the Living Heart is closer to a personalized, highly-parameterized simulation than a continuously streaming IoT system, it highlights the core philosophy of a modern digital twin: moving past static CAD files to create models that are fundamentally aligned with a specific, real-world physical instance.</description><enclosure url="https://jaehyeon.me/blog/2026-04-23-digital-twin-industry-4-0/featured.png" length="73776" type="image/png"/></item><item><title>Building a Real-Time Industrial Digital Twin with Apache Flink and Online Machine Learning</title><link>https://jaehyeon.me/blog/2026-04-21-digital-twin-online-machine-learning/</link><pubDate>Tue, 21 Apr 2026 00:00:00 +0000</pubDate><guid>https://jaehyeon.me/blog/2026-04-21-digital-twin-online-machine-learning/</guid><description>Overview Imagine using a rolling pin to flatten out a thick piece of dough. A Hot Strip Mill does the exact same thing, but with glowing red-hot steel slabs (often heated over 1000°C) and massive mechanical rollers. The steel is passed through a series of these rollers, crushing it down from a thick block into a long, thin sheet.
Calculating the exact Rolling Force required to crush the steel is critical.</description><enclosure url="https://jaehyeon.me/blog/2026-04-21-digital-twin-online-machine-learning/featured.png" length="130846" type="image/png"/></item></channel></rss>