Teams are continually seeking ways to improve performance on and off the track. One key area of focus is the use of simulation to understand the performance of the car and make strategic decisions during races.
Modern Modeling and Simulation Powered by Machine Learning
JuliaSim is the next-generation, cloud-based platform for model-based design. Using modern scientific machine learning (SciML) techniques and equation-based digital twin modeling and simulation, JuliaSim can accelerate simulation by up to 500x.
JuliaSim = Science + ML
JuliaSim is machine learning done right for engineers. Mix scientific knowledge of physical and chemical processes with data to build digital twins that predict better from less data.
Precision Modeling With Digital Twins Means Faster Production and Lower Costs
Building precise and accurate models is difficult and labor-intensive with current tools. JuliaSim’s unified modeling frameworks for integrating machine learning with traditional simulation in order to overcome the challenge of building predictive models. Pre-start the modeling process with our built-in libraries, use machine learning to automatically discover higher order physics, swiftly calibrate the digital twin to real-world data, use neural surrogates to accelerate the simulation, and generate highly accurate nonlinear controls for deploying on embeded devices.
Integrate with Existing Tools
Engineers can use their models from existing tools. JuliaSim is compatible with the industry standard interface used across virtually all modeling tools - the Functional Mock-up Interface. Generate a Functional Mock-up Unit (FMU) from your existing modeling tools (such as Simulink or Dymola), import that FMU into JuliaSim, train an accelerated surrogate model, export a new FMU and import that into your FMI-compliant tools. Teams not using FMUs can use function registration to embed calls to Julia, R, and Python models from within model blocks.
Build Models Faster
JuliaSim is built on the ModelingToolkit acausal modeling platform. [This system allows for building complex models with orders of magnitude less code and with more code reuse than causal modeling platforms](link to acausal vs causal video). Further increase this productivity by integrating one of JuliaSim’s pre-made models, which span domains from two-phase flow HVAC simulation to battery packs.
Transform Models to Digital Twins
JuliaSim has all of the tools necessary to integrate data into the predictive modeling process. Nonlinear calibration, integrated automatic differenitation, and optimal design tools allow for fast and accurate parameter tuning. Meanwhile techniques like universal differential equations allow for automating the discovery of missing higher-order physics to extend models in a data-driven way.
Ensure a Robust Deployment
JuliaSim understands that the biggest impediment to deploying nonlinear model-predictive controllers and integrating machine learning models into the field is guarenteeing safety. JuliaSim has tools to tune nonlinear controllers using robust and uncertainty-aware techniques, analyze the uncertainty and errors of neural surrogate models, and quantify the parametric uncertainty of model fits.
Interested in SciML?
Learn about the impact of SciML on industrial engineering, the benefits of a SciML approach to modeling, and the difference between SciML and other machine learning techniques.
JuliaSim Premade Models
Quickly make realistic physical and chemical models
JuliaSim Digital Echo
Accelerate simulations with our DigitalEcho deep learning architecture
JuliaSim Model Opimizer
Calibrate Models to Data and Discover the Missing Physics
Design Controls for Realistic Non linear Environments
JuliaSim is Simple
Using acausal modeling and pre-made models, scientists and engineers can build realistic models in minutes.
JuliaSim is Fast
10x to 1000x faster than other traditional products. Works seamlessly on GPUs.
JuliaSim Integrates Machine Learning
Use SciML methods to automate the discovery of missing physics, accelerate simulation with neural surrogates, and improve processes with deep learned controllers.
JuliaSim is Safe
With an emphasis on uncertainty quantification, numerical stability, and robust controls, JuliaSim is the safe way to bring machine learning into real-world processes.
Advanced Modeling and Simulation With Simple Code
You have data and a model, and you want the two to match. What do you do?
Register for our upcoming webinar "Model Calibration and Parameter Estimation with JuliaSim Model Optimizer" and learn how model optimization can help you.
LEARN MORE ABOUT JULIASIM AND HOW IT IS BEING USED
Modeling and Simulation with JuliaSim
Dr. Chris Rackauckas
Modeling Battery Lifespan with JuliaSim
Dr. Marc Berliner
SciML: Scientific Computing + Machine Learning = Industrial Modeling for Engineers
Dr. Chris Rackauckas