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, December 5th | 09:30 AM ET (US)
Ingesting and Deploying Functional Mockup Units in JuliaSim
- How to simulate FMUs in JuliaSim using FMI.jl
- How to generate FMUs of JuliaSim models
- Example: vapor compression cycle model
- How to generate FMUs of JuliaSim surrogate models
Ranjan is a sales engineer at JuliaHub, where he helps customers leverage modern engineering workflows and scientific machine learning using JuliaSim. He has a PhD in Mathematics & Computational Science from MIT, where his thesis work centered on surrogate modeling of dynamical systems.
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.