Julia collaboration has taken a revolutionary leap forward with JuliaHub Projects.
Wednesday, April 5th | 10:30 AM ET (US)
Modern Industrial Modeling
Industrial simulation and modeling requires several disciplines outside of the modeling effort itself. These disciplines include data management, version control, parallel computing, reproducibility, and creating artifacts for other team members to consume. Pulling all this together can be hard. JuliaSim and JuliaHub can combine to assist users in all these domains in order to more easily share insights and build better designs.
The JuliaSim package suite
JuliaHub Job Outputs for monitoring and results
Integrating JuliaHub DataSets to capture and version simulation runs
- JuliaHub Pluto Notebooks for sharing analysis
- JuliaHub PrivateRegisteries for sharing packages
Join us to learn how you can modernize your industrial modeling workflow.
Register now to reserve your spot!
Deep Datta recently joined Julia Computing as Product Director. Previously, he was senior product manager at JFrog where he led their partnership team through building DevOps-focused integrations. He also helped the Conan open-source community develop a central repository for C / C++ packages called ConanCenter. With over a decade of startup experience, Deep has worked in product R&D, engineering, and open-source program management.
Choose access and levels of access for team members.
Folders + Files
Select how folders and files persist across products and applications.
Allow individual team members to create their own local branch of the project's content, work independently on code, and merge those changes back to the master project.
Search Everything in One Place
Public & private code all together
Semantic search understands Julia syntax
Regex search for really tricky cases
Deploy Julia Apps
Build interactive apps & deploy easily
Bare Kubernetes app deploys also supported - to the cloud, to existing or air-gapped on-prem k8s clusters
SSO-integrated user auth
Scale with Ease
Grab a 1000-core cluster for Monte Carlo simulations
Use a beefy GPU machine to train ML models
Built-in support for DataSets.jl
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.