Yeah, at the least you can just sample from a gaussian distribution around the nominal value; the shape is probably wrong but it's got to be better than assumin

Yeah, at the least you can just sample from a gaussian distribution around the nominal value; the shape is probably wrong but it's got to be better than assuming no error. Or you could just do error analysis on the formula to find out which terms contribute the most to the final error. I like that your suggestion is in-line with how we now can use an autoencoder to convert a molecular graph into a continuous latent space which can be used to train a network to predict some property. Now we have a latent space where things that have similar property predictions are near each other in a space we can explore and then decode into new candidate materials/molecules. Drug discovery, solar panels, I'm looking forward to the field taking off industrially for sure. Applying machine learning and advanced computational techniques to ad-tech is very depressing with problems like these out there.