How to use latin hypercube sampling? Neural network optimization help?

I have been running simulations for the past few days. It is about a drill simulation, and by changing speed and feed I calculated the material removal rate (MRR) and got the nodal temperatures (NT). My aim is to make a multiobjective optimization where maximizing MRR and minimizing NT depending on the feed and speed. How would i do this in MATLAB? I attached the data if that helps understanding my question.

Thank you in advance!

Answer 1

[Naman Chaturvedi]

Since your simulation is quite time consuming and you are considering sampling methods as well, I would suggest an approach like Surrogate Modelling for your work.

The following link might help you:

https://ccse.lbl.gov/people/julianem/Manual_SOCEMO_matlab.pdf

You can also find some third party toolboxes/codes related to multi-objective surrogate model optimization methods using MATLAB for a use case similar to yours.

Hope this helps!

[Samy Akrouh]

Thank you very much Naman! It seems to be exactly what I am looking for.

Now, my input data are 3 variables and 2 objectives and the data i got is already numerical:

Maximixe F(X)={ f1(x)=x4, -f2(x)=x5} ;; [2000<x4x1000, 15000<x5<85000]

Subject to

-2500 <g1(x)=x1< -1500

-0.6<g2(x)=x2< -0.35

-7 <g3(x)=x3< -3

Being

x1=Speed

x2=Feed

x3=Depth of cut

x4=MRR

x5=NT

Each of them a vector of 25 values (in the attached file).

How would I adapt the datainput_mop2.m for this?

Thank you in advance and sorry for this probably stupid question =D