Hello,

For these kinds of tasks, that is if I am understanding you correctly, a technique called dimensionality reduction is usually used.

Dimensionality reduction is:

"

When dealing with high dimensional data, it is often useful to reduce the dimensionality by projecting the data to a lower dimensional subspace which captures the “essence” of the data. This is called dimensionality reduction.

— Page 11, Machine Learning: A Probabilistic Perspective, 2012.

"

The following link is a good place to start which dives directly into the practical and implementation side of things while explaining each step intuitively in python.

https://machinelearningmastery.com/dimensionality-reduction-algorithms-with-python/

Also, you can use simple algebraic methods too which might not be accurate but considering your space and the complexity between its relations might work considerably well.

Hi,

3D contour plot in OriginLab might be helpful for you.