I think with varying starting dimensions that the solution surfaces will be different and so have differing maxima and minima.Rick! wrote: ↑Sun Oct 13, 2019 7:31 pmJust like in most cases of smart solutions that I have experienced, great ideas typically converge on very similar solutions.
Case in point, this optimized rocker arm. Using good fundamentals, the same answer can be reached through different means.
Except mine is better as it doesn't have stress attracting notches.
The solution space is not infinite in the case of this rocker arm. There will be a few local minimums and maximums in the solution surface with properly defined constraints, design variables and objective goal that funnel the solution into very similar load paths and geometry.
I don't need 50 or 100 generative solutions that are variations of a theme.
The one concerning issue of this kind of tech is having "operators" instead of good engineers applying AI.
If you don't know the fundamentals of physics and mechanics, how do you know the "answer" is right?
I can imagine a CAD or drafting course for engineering students where the instructor presents an exemplar and asks them to emulate it, having a much more "funneled" solution. If two classes were shown different exemplars then the solution sets would be quite different.
It might be interesting to run a generated solution in reverse, i.e provide a larger target mass and have the program add mass. Repeat.
What would this tell you about the A.I.?