How Many Rays Do I Need for Monte Carlo Optimization? CvWEXY_P2
While it is important to ensure that a sufficient number of rays are traced to 3Wxtxk._E
distinguish the merit function value from the noise floor, it is often not necessary to =usDI<3r
trace as many rays during optimization as you might to obtain a given level of lBZ*G
accuracy for analysis purposes. What matters during optimization is that the (NN14
changes the optimizer makes to the model affect the merit function in the same way U`_vF~el~
that the overall performance is affected. It is possible to define the merit function so ER0#$yFpM
that it has less accuracy and/or coarser mesh resolution than meshes used for J}KktD@!O
analysis and yet produce improvements during optimization, especially in the early >Io7h#[u
stages of a design. .p~;U|h"
A rule of thumb for the first Monte Carlo run on a system is to have an average of at = >%%]0
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays cP=mJ1
on the receiver to achieve uniform distribution. It is likely that you will need to ioCkPj
define more rays than 800 in a simulation in order to get 800 rays on the receiver. CyDf[C)=
When using simplified meshes as merit functions, you should check the before and /l%q q*Ew
after performance of a design to verify that the changes correlate to the changes of % peb{i
the merit function during optimization. As a design reaches its final performance U (7P X`1
level, you will have to add rays to the simulation to reduce the noise floor so that Z M, ^R?e
sufficient accuracy and mesh resolution are available for the optimizer to find the 2e@\6l,!^
best solution.
