How Many Rays Do I Need for Monte Carlo Optimization?
%)pP[[h
While it is important to ensure that a sufficient number of rays are traced to g93I +
distinguish the merit function value from the noise floor, it is often not necessary to e &d3SQ%
trace as many rays during optimization as you might to obtain a given level of K n']n91m
accuracy for analysis purposes. What matters during optimization is that the <e'P%tG'
changes the optimizer makes to the model affect the merit function in the same way :FnOS<_B
that the overall performance is affected. It is possible to define the merit function so 6H0W`S0a
that it has less accuracy and/or coarser mesh resolution than meshes used for {5SfE$r
analysis and yet produce improvements during optimization, especially in the early + Qt[1 Xq
stages of a design. P?uf?{
A rule of thumb for the first Monte Carlo run on a system is to have an average of at Y mq3ty]Pe
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays \ 0D$Mie
on the receiver to achieve uniform distribution. It is likely that you will need to |- |jf
define more rays than 800 in a simulation in order to get 800 rays on the receiver. e[s5N:IUd3
When using simplified meshes as merit functions, you should check the before and >&BrCu[u
after performance of a design to verify that the changes correlate to the changes of H \ 3M
the merit function during optimization. As a design reaches its final performance ~NxEc8Y
level, you will have to add rays to the simulation to reduce the noise floor so that iu +3,]7Fm
sufficient accuracy and mesh resolution are available for the optimizer to find the !;i*\
a
best solution.
