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.