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.
Wednesday, November 2nd | 1:00 PM ET (US)
Accelerating Simulations Using JuliaSimCompiler
Are you a simulation engineer looking to optimize your workflow and scale up your models efficiently? Join our exclusive webinar, where we will dive into the powerful capabilities of JuliaSimCompiler and how it can speed up simulation workflows.
In this Webinar, we will address the common hurdles faced by simulation engineers and how to leverage the capabilities of JuliaSimCompiler to enhance your simulation process. By supporting array functions in MTK models, you can achieve faster simulations and streamline your workflow significantly.
What You’ll Learn:
- Speeding up Large MTK Models: Discover how JuliaSimCompiler can significantly accelerate the simulation time for large-scale MTK models, allowing you to run complex simulations faster than ever before.
- Array Functions in MTK Models: Learn how to implement array operations in your MTK models using JuliaSimCompiler, enhancing the efficiency and versatility of your simulations.
- Running Basic Neural Networks in MTK Models: Unlock the potential of basic neural networks in MTK models and explore the possibilities of integrating machine learning techniques into your simulations.
After the Webinar, we will share the recorded video and codes used in the live demo. This will help you gain hands-on access to the practical implementation of JuliaSimCompiler and explore its features at your own pace.
Availability is limited, so make sure to register early to reserve your spot.
Engineering Team Lead
Yingbo Ma is the lead developer of MTK, and the modeling and numerics team lead at JuliaHub.
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.