Hi @martinjankowiak, thank you for taking your time to dig into this, I appreciate it.
I work in bioprocess engineering and I am interested in improving the experimental design process which includes biological contents in the process and process settings. An example of how I would apply it is:
a discrete design space of 3 variables, each having 4 levels of magnitude, e.g.
Temperature - 25, 30, 35, 40
pH - 3, 4, 5, 6
Biological component 1: 5, 10, 15, 20
Biological component 2: 2, 4, 6, 8
There are many other designs we use of different dimensionalities but this would be an example and it would always be discrete. Some results coming from the processes are continuous, but to keep it simple I would do some feature extraction from curves (e.g. a timeseries profile of something) and extract single discrete variables.
As for the model i dont know I am afraid. From the election example I imagine you are refering to the guide? which would be a 3-layer NN. I realised yesterday that in the election example a 51x list is the input to the system. I guess I could essentially make a list of each combination from the design space and the results from their experiments since a NN doesnt care what the logic behind the numbers are? i.e. a 3 variable with 4 levels would be 64 length list?
The desired outcome as mentioned would be to use historical data and a smaller than usual experimental design/seeding experiment to quickly home unto the combination in the space which would result in the highest outcome, in the end reducing experimental runs.
Edit: We cant run 1 experimental combination at a time which is the workflow i often see in solutions. More often we do 15-30 at a time. Due to the time it takes to do the experiments it would not be viable. But from the examples I’ve seen it looks like this wouldn’t be a problem.
Thanks again, hope it was enough answers.