JuliaSim combines the latest Scientific Machine Learning (SciML) techniques, acausal equation-based digital twin modeling and simulation, and the Julia programming language to speed design time by 80x.
Addressing the Challenges of Precision Model Tooling
With current model-building tools, building precise and accurate models which incorporate real-world data is difficult and labor-intensive. Precision tooling is non-standard and it can be challenging to calibrate large, stiff, and highly nonlinear models. In addition, non-linear optimizations require tens of thousands of algorithmic (Monte Carlo) simulations, making traditional computational software performance a bottleneck for completing any engineering design process. And at the end of the process, few tools if any quantify the uncertainty with respect to this workflow.
Optimize & Train FMUs
JuliaSim can take a new or legacy model via the FMU interface to optimize and calibrate it to make your model match real-world physics and data. It incorporates state-of-the-art methods such as differentiable programming and machine-learned surrogates in order to accelerate the process. Its robust optimization techniques allow for uncertainty quantification with respect to the optimization landscape.
Trusted Models
The result? Calibrated digital twins which reduce the need for numerous and iterative lab and field tests, shrinking design time with cost savings that can reach millions. JuliaSim decreases design time by letting you do more on your computer and less in the field.
Looking for Model Predictive Control (MPC)?
Learn about JuliaSim Control in our webinar on MPC, trimming, and linearization in JuliaSim.
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Advanced Modeling and Simulation With Simple Code
BUILD
MODELS
GENERATE
MODELS
FROM DATA
ACCELERATE
SIMULATIONS
TUNE
CONTROLLERS
Planning Space Missions
The Brazilian National Institute for Space Research (INPE) is the Brazilian government’s research institute for planning space missions
Case Study
Artificial Intelligence and Machine Learning
LakeTide uses Julia for data science and product development
Case Study Emergency Medical Supplies by Drone
Zipline uses Julia for aircraft simulation to deliver life-saving emergency medical supplies via drone in Africa and worldwide
Case Study 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.
Watch Now LEARN MORE ABOUT JULIASIM AND HOW IT IS BEING USED
Welcome to JuliaSim: An Introduction
Dr. Chris Rackauckas
WEBINAR
Building Digital Twins with JuliaSim
DR. RAJ ABHIJIT DANDEKAR
WEBINAR
Accelerating Functional Mock Up (FMU) Models
JACOB VAVERKA
WEBINAR
Modeling Tools
Simulate with speed and accuracy
- Efficient: outperforms C and Fortran methods
- Full-featured: use all of the latest techniques
Model symbolically, let the power of Julia simplify, accelerate, and parallelize
- Acausal component-based modeling
- High-Performance: let Julia generate the fastest possible code for the model
Model intuitively, but get the speed, performance, and features of a skilled coder
- Build models graphically, no code required
- Intuitively use all of the features of JuliaSim
Fast chemical reaction modeling, no hassle
- Concise chemical reaction notation
- Generate ODEs, SDEs, and Gillespie jump processes
Model Libraries
Don’t start a model from scratch: build using a common set of components
- Quickly get real-world models running
- Worry about the engineering, not the math
Buildings use 40% of the world’s energy, don’t let it use that much of yours
- Fast and accurate refrigerant property models
- Everything from the ground up
Plug complete models of electrical devices into any physical system
- Industry-standard MOSFET models
- Import Spectre/SPICE netlists and direct schematics
Your domain-specific library
- With additional libraries in progress, reach out to us to discuss what library you might need to take advantage of JuliaSim
Special Solvers
Discontinuity-Aware DAE Solver - Handle phase transitions, contacts, and more with ease
- Accelerate differential equation solves with implicit discontinuities
- Increase the stability and accuracy of difficult simulations
Accelerated Nonlinear Solver - Find your inner zen by reaching equilibrium faster
- Fast, specialized algorithm for solving steady-state equations
- Proprietary algorithmic enhancements for improved performance
Symbolic-Numeric BDF Solver - Faster and more robust stiff ODE and DAE solving
- Integrated ModelingToolkit.jl simplifications for more stability and performance from pure numerical methods
Modules
You bring the data, we give you the physical model
- Quickly generate predictive models from data
- Connect data-generated models to physics
You bring physics, we bring machine learning, for fast simulation
- Accelerate large simulations with machine learning-surrogates
- Connect surrogate models in Modeling Toolkit
Quickly convert models and data into calibrated systems with quantified uncertainties
- Model calibration with automated parallelism and uncertainty quantification
- Model autocomplete by using scientific machine learning (SciML) to discover missing physics
Take control of your hardware, explore your nonlinearities
- Linearized controls analysis
- Construct and calibrate Model-predictive controllers
Learn from your data, let the digital physics evolve
- Real-time model calibration from stream data
- Machine learning in the cloud – adapt over time
Apps
Drop an FMU in the mail, pick it up 1000x faster
- Import FMUs simulations from common tools
- Point and click machine learning returns a 1000x faster simulator
Making everyday engineering simpler
- Point-and-click optimization of PID parameters
- Robust sensitivity analyses included
Point and click digital twin generation, made for the engineer
- Import data from standard file formats
- Use machine learning without the hassle
Ready to see how JuliaSim can accelerate your product development? Speak to a member of our team.
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