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
Julia collaboration has taken a revolutionary leap forward with JuliaHub Projects.
Tuesday, September 26th | 11:30 AM ET (US)
Guide to Parallel Computing with Julia
Are you looking to enhance the performance of your programs and unlock the true potential of Julia? Join us for an insightful Webinar as we delve into the world of parallel computing in Julia using JuliaHub. We will explore the powerful world of parallel programming in Julia and also showcase the use of Asynchronous Tasks for concurrency, multithreading macros, and the Distributed standard library for parallelism, all while focusing on JuliaHub as the cutting-edge platform for this purpose.
What You'll Learn:
- Use Tasks / Channels for Coroutines: Discover how to effectively use Tasks and Channels to enable smooth communication between different components of your program, unleashing the potential of coroutines for seamless parallel computing.
- Optimize for a Single Machine Using Spawn and Thread Macros: Learn to optimize your code for a single machine by harnessing the power of Julia's spawn and thread macros, allowing you to efficiently utilize available resources.
- Leverage Multiple Machines with Distributed.jl: Explore the world of distributed computing with Distributed.jl, enabling you to scale your computations across multiple machines, each operating within its memory space.
Availability is limited, so make sure to register early to reserve your spot.
JuliaHub and JuliaSim sales engineer with mathematics background and experience building applications in high performance compute environment for production automotive workflows.
Dr. Jeff Bezanson
Co-creator of the Julia programming language & Co-founder of JuliaHub
Jeff Bezanson is a co-founder and the CTO of Julia Computing. Jeff has a long history of experience in the field of computing, dating back to their days as a graduate student at MIT. Jeff then went on to work as a senior software engineer at Interactive Supercomputing, where they gained valuable experience in the development of high-performance computing software. After that, they founded their own company, Computing Tools, LLC, which developed the Julia programming language.
Jeff Bezanson has a Master of Science (M.S.) in Computer Science from the Massachusetts Institute of Technology and a Bachelor of Arts (B.A.) in Computer Science from Harvard University.
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.