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The modern platform for technical computing

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


In this webinar, we delve into the world of advanced Dash App development using Dash.jl, leading to the deployment of a dynamic flight traffic visualization on JuliaHub.

Maja Gwóźdź will showcase the capabilities of Dash.jl building a flight traffic app from scratch. Learn how to use Dash.jl's powerful features such as map integration and other interactive elements that bring data to life.

One of the focal points of this webinar is dataset preparation, essential for seamless integration with the application. Learn how to manage datasets seamlessly ensuring optimal performance and user experience. Maja will also take you through the versatility of our template that can be adapted to diverse project needs.

Discover the versatility of our meticulously designed template, poised for adaptation to diverse project requirements. Whether you're envisioning a similar visualization or exploring entirely different domains, our template serves as a robust foundation for your creative endeavors.

Key Takeaways:

  • Master the art of building and deploying advanced Dash Apps with Dash.jl

  • Construct a flight traffic visualization from inception to deployment on JuliaHub

  • Learn the nuances of dataset preparation for seamless integration

  • Harness the power of reusable templates for future projects

  • Gain live demonstration insights for an enriched learning experience



Meet Your Speaker


Maja Gwozdz

Maja is currently pursuing her Master of Science degree in Theoretical Computer Science at ETH Zurich, she has accumulated almost three years of hands-on experience at JuliaHub. Her internship at the Swiss National Supercomputing Centre allowed her to delve deeper into Julia programming, resulting in the development of a new package designed to simplify I/O tasks.

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.