Building Real-Time Digital Twins with desflow on Jaehyeon Kim

Defining Data-Streaming Simulations in YAML, Without Writing Python

Tue, 06 Oct 2026 00:00:00 +0000

A pipeline is easier to test when its input behaves like production: orders arrive at random, queues grow, a machine slows down. A simulation can produce that data, but when the simulation is a Python program, every change is a code change. Someone who only wants to double an arrival rate has to read and edit code, and a reviewer has to check that nothing else moved. A file that holds only settings is easier to read, to compare in a pull request and to run in CI.

desflow 0.16.0 (then named dynamic-des) adds that file. A YAML blueprint describes the whole simulation, and the desflow command runs it. Each of the seven examples in the repository now has a YAML version in plain YAML. The documentation was rebuilt around the three ways to write a simulation.

desflow: A Declarative API with Postgres and Redis Connectors

Fri, 17 Jul 2026 00:00:00 +0000

Simulated data is most useful when the code that produces it is easy to read, and when it reaches the same places as production data, such as a PostgreSQL table or a Redis stream. Since I wrote about Dynamic DES v0.8.1 and its data lake support, the project has grown in both directions: a cleaner way to write a simulation, and more places to send and receive data.

Here is what changed on the way to v0.11.1.

One Simulation, Two Pipelines: Batch Training and Live Inference with desflow

Mon, 25 May 2026 00:00:00 +0000

Training a machine learning model on simulated data is straightforward until you try to deploy it. The disconnect usually happens at the pipeline level: training requires massive, historical batch data (like Parquet files in an S3 bucket), but production inference requires real-time, event-driven streams (like Kafka or Redis). Maintaining two separate simulation codebases, one for generating training data and another for streaming live events, introduces friction, schema mismatches, and duplicated engineering effort.

Building an Event-Driven Hybrid Digital Twin with desflow

Wed, 29 Apr 2026 00:00:00 +0000

Asynchronous Gap A true Hybrid Digital Twin does more than just mirror reality. It actively forecasts the future by running a simulation against live operational states. We established this in Part 1. 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.

Why Digital Twins Are Rewiring Industry 4.0

Wed, 22 Apr 2026 00:00:00 +0000

A simulation and a digital twin are often treated as the same thing, but they are built differently. A traditional simulation runs offline on assumed inputs. An operational digital twin stays aligned with one real system as it changes. This post walks through the layers that separate the two, and an event-driven hybrid that combines them. Beyond CAD Models There is a project by Dassault Systèmes called the Living Heart that illustrates the trajectory of this technology.

Building a Real-Time Industrial Digital Twin with Apache Flink and Online Machine Learning

Tue, 21 Apr 2026 00:00:00 +0000

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.