heatmapping.org but is more oriented toward deep learning. If at some point you are interested, I recall a paper called "deep Taylor decomposition."
To get the best answer, try a Google search that includes the name Sobol:
Sobol method neural network
When I do that, a lot of info came up. For example: https://annals-csis.org/Volume_11/drp/pdf/225.pdf
I would also recommend searching on techniques for variable importance estimation of neural networks, etc. by the biomedicine / medical communities. Their work is readable, high quality, and investigates all the nuances of the algorithms and their predictions, mainly because it is mission-critical, experiences heavy oversight, and to discover new science with the aid of a ML algorithm.
I want to point out that in most data science / ML cases, at least for me, there can be many possible solution models that fundamentally have unphysical construction. I use the variable importances to help drive new science and determine if the model's underlying structure is even plausable or if it disagrees with physics constraints.
I would recommend reading a bit about the Fredholm Equation of the First kind (a several hundred year old math relationship puzzle), which btw lays out SVMs and other machine learning constructs. Numerical Recipes in C has the best write-up that I've seen.
Look up LINIPOS, which stands for linear inverse problems with probability * * forgot the last two words and a statistical solution to the Fredholm equation of the first kind known as the Vardi-Lee algorithm.
I seemed to have rambled but those last 2 paragraphs are closely related to variable importance estimates.
