Product-development notebook: latent-variable model inversion walkthrough

[kgdunn]

Migrated from TODO.md (Experiments / DOE section).

Build out the product-development notebook that walks through latent-variable model inversion for new-product design. (TODO.txt)

The storyboard narrative, preserved verbatim from the original TODO.txt:

Output
3 dofs available in general
1 dof
2 dof.

PCA space. For outputs. On 7 properties A to G.
Shows the correlations and quickly seeing gaps

PCA on processing settings and blend properties. RX. Related to the scores. Often a 1 to 1 relationship of the outputs to the scores. So this is valid.

Use the 5 properties of A to E from foods data set. Show that constraints are also possible. Indicates multiple solutions. Also mention the nulll space. Of multiple solutions. T -> X.

Then go to the cinac case study. Show that the lvs are interesting and have meaning. We can design new product here. Y -> T.

Now we can also go directly from Y to X, with a NLP solution. But it goes via the scores T. And it can handle additional constraints.
Would be cool to demo this.

Lastly how do you start out?
* dB is partially available.
* R selected from a regular mixture DoE.
* Calculate XR. Do a doe in this space to select a subset of experimental conditions.
* run those expts.
* Start up your mixture model
* See where there are holes literally in the spaces.
* Use LV model inversion to find which process settings and ratios and materials are needed to fill in those gaps
* Targeted DoE.


raw material properties affect what final product properties is made clear in the pls loadings plot. Again, something that is not seen in NN model.

Build a model explorer for the pls model of muteii for the rubbers, showing how the blend properties correlated with the 8 outputs.

Show the approach on p 23 of muteii paper for how to start out. You can use what you have and add to it via a d optimal doe.
Show figure 4 and emph that the full 111 expats were not needed. Only 17.