In this hands-on webinar, our expert team will walk you through getting started with using ModelingToolkit.jl to solve real-life problems. To start we’ll show the fundamentals of defining variables, parameters, and equations to solve a steady-state nonlinear problem. Then we’ll add time to the problem to show how we can also solve a set of differential equations. ModelingToolkit.jl can be used in this way to solve a flat system of equations, however, this quickly becomes cumbersome for more complex real-life systems. Acausal Modeling is then introduced to show how complex models can be constructed by assembly of components. Components are defined using the fundamentals previously covered. Finally, with the understanding of how to build components and connect them to create systems, we’ll cover a couple of fundamental industrial examples, such as a DC motor and Mass-Spring-Damper.

ModelingToolkit.jl is an open-source symbolic-numeric modeling Julia package. 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. It bridges the gap between many different kinds of equations, allowing one to quickly and easily transform systems of DAEs into optimization problems, or vice-versa, and then simplify and parallelize the resulting expressions before generating code.