2024-07-09 –, TU-Eindhoven -1.354
Acausal modeling tools such as ModelingToolkit, Modelica, and Simscape are widely used for industrial modeling, simulated real-world phenomena such as hydraulic, HVAC, and multibody systems. In this tutorial we will dive into how to easily build large-scale high-fidelity models with ModelingToolkit and use its embedding within the Julia programming language in order to transform high level descriptions into simpler equations and simulate the resulting models with the tools of Julia's SciML.
ModelingToolkit is the symbolic modeling layer of Julia's SciML. SciML covers many areas of numerical modeling and simulation, such as linear systems of equations, nonlinear systems of equations, differential equations, and optimization. Similarly, ModelingToolkit is a symbolic modeling language which covers the same interfaces and shuttles to the underlying numerical solvers.
What makes ModelingToolkit interesting is its ability to allow for reusable component-based modeling and its ability to perform automatic simplification of the resulting equations. This allows for efficient hierarchical modeling, where libraries of components define items like engines and air compressors, and then modelers can easily build large models by connecting the existing physical components to generate composed models. The resulting composed model is then simplified to give a representation of he system that is fast to simulate, but without being slow to model.
In this workshop we will showcase how to build steady state models, transient models, and optimization models. We will showcase the ModelingToolkitStandardLibrary as a source of pre-built components that can be used to quickly get started, and we will show how ModelingToolkit interacts with the growing set of tooling around it, including graphical user interfaces (GUIs), specialized solvers, and model analysis tooling. This includes:
- Moment closure expansions
- File readers for importing models (SBML, CellML)
- MathOptInterface special integrations for Optimization
- Structural identifiability analysis
- Linearization
and much more.
Dr. Chris Rackauckas is the VP of Modeling and Simulation at JuliaHub, the Director of Scientific Research at Pumas-AI, Co-PI of the Julia Lab at MIT, and the lead developer of the SciML Open Source Software Organization. For his work in mechanistic machine learning, his work is credited for the 15,000x acceleration of NASA Launch Services simulations and recently demonstrated a 60x-570x acceleration over Modelica tools in HVAC simulation, earning Chris the US Air Force Artificial Intelligence Accelerator Scientific Excellence Award. See more at https://chrisrackauckas.com/. He is the lead developer of the Pumas project and has received a top presentation award at every ACoP in the last 3 years for improving methods for uncertainty quantification, automated GPU acceleration of nonlinear mixed effects modeling (NLME), and machine learning assisted construction of NLME models with DeepNLME. For these achievements, Chris received the Emerging Scientist award from ISoP.
Michael Tiller has a Ph.D. in Mechanical Engineering from the University of Illinois, Urbana-Champaign. He is the Secretary of the Modelica Association, President of the North America Modelica Users' Group, author of two books on Modelica and currently Senior Director of Product Management for JuliaSim at JuliaHub.