ModelingToolkit.jl is a modeling language for high-performance symbolic-numeric computation in scientific computing and scientific machine learning. It then mixes ideas from symbolic computational algebra systems with causal and acausal equation-based modeling frameworks to give an extendable and parallel modeling system. It allows for users to give a high-level description of a model for symbolic preprocessing to analyze and enhance the model. Automatic symbolic transformations, such as index reduction of differential-algebraic equations, make it possible to solve equations that are impossible to solve with a purely numeric-based technique. ModelingToolkit.jl is a symbolic-numeric modeling package. Thus it combines some of the features from symbolic computing packages like SymPy or Mathematica with the ideas of equation-based modeling systems like the causal Simulink and the acausal Modelica.
Features
- Causal and acausal modeling (Simulink/Modelica)
- Automated model transformation, simplification, and composition
- Automatic conversion of numerical models into symbolic models
- Composition of models through the components, a lazy connection system, and tools for expanding/flattening
- Pervasive parallelism in symbolic computations and generated functions
- Transformations like alias elimination and tearing of nonlinear systems for efficiently numerically handling large-scale systems of equations
Project Samples
