How Many Rays Do I Need for Monte Carlo Optimization? R6->t #n,
While it is important to ensure that a sufficient number of rays are traced to \9T7A&
distinguish the merit function value from the noise floor, it is often not necessary to 8":Q)9;%
trace as many rays during optimization as you might to obtain a given level of mC#>33{
accuracy for analysis purposes. What matters during optimization is that the =I_'.b
changes the optimizer makes to the model affect the merit function in the same way % pCTN P
that the overall performance is affected. It is possible to define the merit function so ;$g?T~v7
that it has less accuracy and/or coarser mesh resolution than meshes used for Nh44]*
analysis and yet produce improvements during optimization, especially in the early R:qW;n%AF
stages of a design. ~D>p0+-c
A rule of thumb for the first Monte Carlo run on a system is to have an average of at S_H+WfIHV'
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays [nq@m c~<
on the receiver to achieve uniform distribution. It is likely that you will need to OjA,]Gv6
define more rays than 800 in a simulation in order to get 800 rays on the receiver. 5b7RY V
When using simplified meshes as merit functions, you should check the before and a%0EiU
after performance of a design to verify that the changes correlate to the changes of p]c%f 2E>d
the merit function during optimization. As a design reaches its final performance 5 z)~\;[ -
level, you will have to add rays to the simulation to reduce the noise floor so that J{G?-+`
sufficient accuracy and mesh resolution are available for the optimizer to find the A04U /;
best solution.
