Differential Equations Versus Machine Learning
Define your own rules or let the data do the talking?
Let’s compare and contrast differential equations (DE) to data-driven approaches like machine learning (ML).
In a nutshell — albeit with caveats — they can be thought of different approaches to modelling various phenomena. Do you make the rules? Or should you let your juicy data learn the rules for you?
Both types of models absolutely drive the world around us. Let’s dig in.
Edit: I have written a COVID-19-themed sequel. Students of data science may also find my other articles here, here, here, here and here helpful.
Example DE models
Navier-Stokes (meteorology)
The model behind weather predictions. It is a chaotic model — meaning predictions can be wildly off when using just slightly incorrect inputs. That’s why weather predictions are often wrong! Simulations are carried out with super-computers.
