cuTile.jl Brings NVIDIA’s CUDA Tile to Julia: NVIDIA Developer blog recently shared the early release of cuTile.jl, a Julia package that makes it possible to program CUDA GPUs using a new tile-based abstraction. Read the full technical breakdown and learn how to get started.
Hands-on Modeling & Simulation Workshops in Europe: Dr Michael Tiller will be conducting workshops in Europe, at Nuremberg(March 20) and the Williams Racing campus at Grove(March 27) to demonstrate modeling and simulation practices using Dyad, an AI-native simulation and modeling platform. Participants will learn how AI-assisted tools, symbolic modeling, and Scientific Machine Learning (SciML) can accelerate system design and analysis. Seating is limited and email confirmation is required to reserve a spot. You can register for Nuremberg and Grove.
Dyad 2.2 — Introducing the Unified Sidebar & Other Updates: Dyad 2.2 focuses on improving workspace organization and AI reliability. A new primary sidebar centralizes access to components and analyses to simplify navigation. We’ve also added real-time summaries for AI agent actions and updated our solver interface for more direct control over simulations. The update is rounded out with a guided migration tool to help you easily move existing projects to the new version. Check out our blog for the full details.
JuliaHub 26.1: Elevating Enterprise Security and Developer Autonomy: JuliaHub 26.1 introduces powerful capabilities to enhance enterprise security, developer productivity, and platform governance. From Windows workstation hibernation and distributed log filtering to automated compliance reporting and expanded admin controls, the release enables teams to work more efficiently while maintaining rigorous enterprise standards. Find out more.
From "Works on My Machine" to "Works on the Cluster": Project Batch Jobs: With Project Batch Jobs, introduced in the latest release of JuliaHub, running large-scale jobs is now as seamless as working locally. This update ensures your exact development environment is reproduced on cluster nodes using your Project.toml and Manifest.toml, eliminating dependency drift. Find out more.
Dyad AI Modeling Livestream: Join our new livestream where Dr. Chris Rackauckas builds real-world models with Dyad’s agentic AI. Bring your own challenge and watch models come together live, showcasing rapid model generation, iteration, and validation. Streaming live on JuliaHub YouTube and @ChrisRackauckas Twitch, the series creates an open lab for engineers and researchers to explore agentic simulation firsthand. https://www.youtube.com/channel/UCvZxpJZ6_4j63ZWCbxdFzdA/live
Universal Differential Equations for Scientific Machine Learning Reaches 1000+ Citations: This paper by Christopher Rackauckas and collaborators is one of the foundational works behind the field of scientific machine learning (SciML). It introduces a formal framework for combining traditional scientific models (physics, biology, engineering equations) with modern machine learning techniques, particularly neural networks. The paper has reached 1000+ citations, marking an important milestone for the field of Scientific Machine Learning. The ideas from this paper continue to shape research across domains ranging from biological discovery to complex engineering systems, highlighting the growing impact of hybrid modeling approaches in scientific computing.
Random Dot Product Graphs as Dynamical Systems: Limitations and Opportunities: This paper by Giulio Valentino Dalla Riva shares a novel approach to understanding how complex networks, from ecosystems and economic markets to social behaviors, evolve over time. Rather than simply predicting future network states based on past data, the researchers aim to discover the underlying mathematical equations (differential equations) that govern why these network changes happen. The base idea explores whether one can leverage SciML to recover the differential equations that guide the system.
Qiskit.jl Julia-language Binding and Wrapper for Qiskit C API: The new Qiskit C API makes it easier to use quantum computing tools beyond Python. A notable example is Qiskit.jl, a Julia-language binding that provides a straightforward way to build and transpile quantum circuits using an API that closely mirrors the Python version.
Tachikoma.jl — A terminal UI framework for Julia: Tachikoma.jl is a pure-Julia framework for building rich, interactive terminal applications. It uses an Elm-inspired Model/update!/view architecture with a 60fps event loop, double-buffered rendering, 30+ composable widgets, and built-in recording/export. Find out more.
Free Online Julia Webinars from JuliaHub: JuliaHub provides free one-hour Webinars led by JuliaHub staff and other experts. Space is limited and registration is required, so please sign up today!
Julia for Engineers Part 2: Modeling Steady-State and Dynamic Systems with Dr. Ranjan Anantharaman, Wednesday March 18, 1-2 PM Eastern(US)
What Makes a Good Engineering Model with Dr. Ranjan Anantharaman, Thursday April 9, 1-2 PM Eastern(US)
Recent JuliaHub Webinars: JuliaHub provides free one-hour Webinars on topics of interest to Julia users. Nearly 100 past Webinars are available online. Click here to watch.
This Month in Julia World: This Month in Julia World is a newsletter from Stefan Krastanov with up-to-date information about Julia events, new releases and more. Read it here.
Nouvelles Julia - Julia News en Français: Nouvelles Julia is a newsletter in French with the latest Julia news. Read it here.
JuliaCon 2026: JuliaCon 2026 will take place August 10-15 in Mainz, at the Johannes Gutenberg University
Julia4PDEs-2026: A 2-day workshop bringing together the Julia community working on partial differential equations (PDEs) will take place on March 26–27, 2026 at Vrije Universiteit Amsterdam. The event will feature keynotes from projects like Ferrite.jl, Gridap.jl, Trixi.jl, WaterLily.jl, and more, along with a hands-on demo of GalerkinToolkit.jl, a new multi-platform finite element library for Julia. Register here.
New Blog Posts from SciML: SciML has published several new blog posts. You can read them here.
Blog Posts from Dr. Chris Rackauckas and Great Lakes Consulting: Read blog posts by JuliaHub VP of Modeling and Simulation Dr. Chris Rackauckas and Great Lakes Consulting Senior Julia Developer Steven Whitaker.
Julia Dispatch Podcast: Julia Dispatch is a Julia podcast from Dr. Chris Rackauckas (JuliaHub VP of Modeling and Simulation) and Dr. Michael Tiemann. Watch it here.
JuliaHub Digital Twin Solutions and Consulting: We help enterprises build deployable and scalable solutions leveraging SciML to create highly accurate and trustworthy Digital Twins. Applications span asset health monitoring, optimization and predictive maintenance, process optimization, model-based control, design optimization, and internal or external simulation tools. We also offer consulting and technical support. Schedule a consultation with our solutions team to discuss your use case.
Careers at JuliaHub: JuliaHub is a fast-growing tech company with fully remote employees in 20 countries on 6 continents. Click here to learn more about exciting careers and internships with JuliaHub.
Julia Expertise Needed at University of Glasgow: Dr. Eric Silverman, Research Fellow at the University of Glasgow, seeks a Research Associate for a 5-year research project on computational modeling for public health using an agent-based modeling framework developed in Julia. Julia experience and a PhD are required for this position. Click here for more information and to apply.
Julia Blog Posts
Ray Tracing in Makie: From Research Data to Photorealistic Renders (Makie Blog)
When a Workshop Starts Behaving Like Infrastructure (Joao Pinelo)
From "Works on My Machine" to "Works on the Cluster": Project Batch Jobs (Mridul Upadhyay)
Image Manipulation with Convolution Using Julia (Ahmad Hamze)
Dates And Times In Julia (Emma Boudreau)
cuTile.jl: Bringing NVIDIA's Tile-Based GPU Programming to Julia (JuliaHub)
JuliaHub 26.1: Elevating Enterprise Security and Developer Autonomy (Mridul Upadhyay)
This Month in Julia World (Feb 2026) (Stefan Krastanov)
Nouvelles Julia (Feb 2026) (Pierre Navaro)
Julia and JuliaHub in the News
NVIDIA Developer: cuTile.jl Brings NVIDIA CUDA Tile-Based Programming to Julia
Design News: JuliaHub's Dyad AI Brings Agentic Intelligence to Physics-Based Engineering
TechTarget:18 Data Science Tools to Consider Using in 2026
How-to Geek: 6 Niche Programming Languages Developers Secretly Love
Upcoming Julia and JuliaHub Events
Online: Julia for Engineers Part 2: Modeling Steady-State and Dynamic Systems with Dr. Ranjan Anantharaman, Wednesday March 18, 1-2 PM Eastern(US)
Online: What Makes a Good Engineering Model with Dr. Ranjan Anantharaman, Thursday, April 9, 1-2 PM Eastern(US)
Nuremberg, In-Person Workshop: Modern Modeling Workflows for Complex Engineering Systems with Dr. Michael Tiller, Friday, March 20 at Nuremberg from 10:00–14:00 (followed by open discussion)
Grove, Oxfordshire, In-Person Workshop: Modern Modeling Workflows for Complex Engineering Systems with Dr. Michael Tiller, Friday, March 27 at the Williams Racing campus from from 10:00–14:00 (followed by open discussion)
Dubrovnik, Croatia: Population Approach Group Europe with JuliaHub, June 2-5
Johannes Gutenberg University Mainz (Germany, near Frankfurt): JuliaCon 2026 with JuliHub, August 10-15
Contact Us: Please contact us if you want to:
Obtain pricing for Dyad and Julia consulting projects for your organization
Schedule Dyad or Julia training
Share information about exciting new Julia case studies or use cases
Partner with JuliaHub to organize a Julia event online or offline
About JuliaHub, Julia and Dyad
Dyad combines physics-based modeling with scientific machine learning(SciML) for mission-critical engineering. Dyad is fully agentic in its design, making it possible for engineers to carry out complex workflows through natural language interaction. Dyad integrates code, diagrams and agentic workflows in a seamless tool driving 10x productivity. Leveraging the Julia and the SciML ecosystem under the hood, Dyad also benefits from significantly higher performance compared to the competition, often being 100x faster at simulating complex physics. Teams leverage Dyad to build smarter, faster, and more reliable systems without compromising the rigor of traditional engineering, supporting use cases from predictive maintenance to real-time performance tuning and over-the-air updates. Get started here.
JuliaHub is a fast and easy-to-use code-to-cloud platform that accelerates the development and deployment of Julia programs. JuliaHub users include some of the most innovative companies in a range of industries including pharmaceuticals, automotive, energy, manufacturing, and semiconductor design and manufacture.
Julia is a high performance open source programming language that powers computationally demanding applications in modeling and simulation, drug development, design of multi-physical systems, electronic design automation, big data analytics, scientific machine learning and artificial intelligence. Julia solves the two language problem by combining the ease of use of Python and R with the speed of C++. Julia provides parallel computing capabilities out of the box and unlimited scalability with minimal effort. Julia has been downloaded by users at more than 10,000 companies and is used at more than 1,500 universities. Julia co-creators are the winners of the prestigious James H. Wilkinson Prize for Numerical Software and the Sidney Fernbach Award.
