This issue will be used to track common interface option handling. This is a constant time factor so it's not the biggest deal, but I feel that we can improve some applications by reducing common latency here.

Issue:
NeuralODE when driven by a batch input will output an array in which the last index is the time index not the batch index. This is different than the standard convention in all other ML libraries including Flux, TensorFlow, Pytorch, and Chainer.
In Flux.conv the batch output dimensions are
( Data-dimension, channel index, batch index)

the time series output from a NeuralODE is anala

It would be helpful to have some text on how contributions should be made.
This includes how to setup the development environment and how to run the tests.

I'm attempting to run the example posted on the GitHub page for diffeqpy. I've installed diffeqpy successfully on a Mac Pro (2019) running Mac OS X 10.15.4 (Catalina). I'm running the Anaconda distribution for Python ver. 3.7.6.

The code I'm running is the following:

#!/Users/user/opt/anaconda3/bin/python
from diffeqpy import de

def f(u,p,t):
    return -u

u0 = 0.5
tspan = (0.,

The current documentation can be misleading regarding which features are ready vs in-progress. I'm not sure if there's a standard practice for this, but if we can summarize the current state in this issue I'll update the README.

Relatedly, we might consider adding a header to the README that warns users that this library is still a WIP, and not to depend on it for anything critical. Again, not

BVProblem and TwoPointBVProblem should accept a function initialGuess( t ) and/or a solution structure in order to reuse solutions as initial guess for continuation.
The documentation on this should be improved/ updated accordingly.