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The modern platform for technical computing

SciML: Scientific Computing + Machine Learning = Industrial Modeling for Engineers 

Understand how robust MPC takes parameter uncertainty into account ensuring that the controller achieves good performance and constraint satisfaction despite uncertainty in the model

Register for Webinar Now

Julia collaboration has taken a revolutionary leap forward with JuliaHub Projects.

Wednesday, October 11th | 2:00 PM ET (US)

APIs and Custom Julia Development on JuliaHub 

Join us for a transformative webinar that unveils the incredible potential of JuliaHub.jl. Dive deep into the world of APIs and custom software development as we showcase how you can seamlessly interact with JuliaHub's versatile ecosystem.

In this webinar, you will learn:

  • How to make your Julia session talk to a JuliaHub instance (authentication etc).
  • How to use JuliaHub.jl to start jobs and to manage datasets.
  • How to use JuliaHub.jl in jobs to have jobs interact with the platform (e.g. to upload datasets from jobs, to start sub-jobs).

After the webinar, you'll have access to valuable resources, including a slide deck and comprehensive package documentation. Don't miss this opportunity to revolutionise your JuliaHub journey.

Register now and embark on a new realm of possibilities!

Register now!



Meet Your Speaker


Morten Piibeleht

Software Engineer

Morten Piibeleht has a STEM PhD and has pursued a career in software development with an concentration in compute. Morten has a particular awareness of the nature and challenges of data science type work. He works on the JuliaHub platform as a software engineer, writes and maintains the JuliaHub.jl package.


JuliaSim Control 

  • Model-predictive control (MPC) for linear and nonlinear systems

  • Robust MPC for uncertain systems

  • GUI apps for autotuning and model reduction

  • PID autotuning to automate workflows and quickly tune PID controllers

Model Discovery

Combine models with tools like DiffEqFlux and NeuralPDE to discover missing physics and generate digital twins.

Combine with Pre-Built Models and Digital Twins

Grab complete models from the JuliaSim Model Store and compose the pieces to accelerate the design process.

Specialized Numerical Environments

Use the latest numerical tools, like discontinuity-aware differential equation solvers, high-performance steady state solvers, and domain-specific environments.

Blending classical physical modeling with modern Scientific Machine Learning techniques.


JuliaSim is a next generation cloud-based simulation platform, combining the latest techniques in Scientific Machine Learning with equation-based digital twin modeling and simulation. Our modern ML-based techniques accelerate simulation by up to 500x, changing the paradigm of what is possible with computational design. The premise of the software is to facilitate the design and accelerate challenging real-life models of considerable complexity.


JuliaSim allows the user to import models directly from the Model Store (more information below) into the Julia environment, making it easy to build large complex simulations. The user-friendly GUI facilitates the process and makes simulation more accessible to a wider audience.


JuliaSim produces surrogates of blackbox (and regular) dynamical systems using Continuous Time Echo State Networks (CTESNs). This novel technique allows, amongst other features, for implicit training in parameter space to stabilize the ill-conditioning present in stiff systems.

Learn more about the JuliaSim Ecosystem

Julia Computing delivers JuliaSim as an answer to accelerating simulations through digital-twin (or surrogate) modeling. By blending classical, physical modeling with advanced scientific machine learning (SciML) techniques, JuliaSim provides a next-generation platform for building, accelerating, and analyzing models.