Inspired by machine learning where you have data and target values. I wondered. How do differential equations make sense to a network.

The way I see it. Differential equation solving takes way too few boundary values. Its pretty much just the amount to get the equation = 0. That is. A zero valued difference.

In machine learnig you ?usually have a lot target values.

So a differential equation is just a target value replacement. Missing values ?replaced by an Implicit function. A simplification.

How can this make sense to machine learning?

For machine learnig we could have missing values. Maybe we should treat every target value as a boundary value and have an implicit equation for some ?smart interpolation range.

**Another idea would be to do research on how to come up with algorithms. Just use machine learning. To see what can be done and then make a similar ?somewhat human understandable algorithm. This goes for programming code also. Machine learning could give ?hints on design.**