Carl Julius Martensen
Julius is currently a PhD candidate at the Otto-von-Guericke University in Magdeburg and an Intern at Pumas-AI. His research evolves around data-driven system identification using scientific machine learning.
In this talk, we will address the problem of data-driven estimation and approximation of completely or partially unknown systems using DataDrivenDiffEq.jl.
We will start by giving a short introduction to the field of symbolic regression in general followed by an example of its practical use.
Here we learn how to
(a) set up a DataDrivenProblem,
(b) use ModelingToolkit.jl to incorporate prior knowledge,
(c) use different algorithms to recover the underlying equations.