How Many Rays Do I Need for Monte Carlo Optimization? tI{
n!
While it is important to ensure that a sufficient number of rays are traced to {
j&|Em]
distinguish the merit function value from the noise floor, it is often not necessary to UH%?{>oRh
trace as many rays during optimization as you might to obtain a given level of jDgiH}
accuracy for analysis purposes. What matters during optimization is that the $. /JA) `
changes the optimizer makes to the model affect the merit function in the same way :XB eGNI*#
that the overall performance is affected. It is possible to define the merit function so pwd7I
that it has less accuracy and/or coarser mesh resolution than meshes used for 4p>@UB&U
analysis and yet produce improvements during optimization, especially in the early 1. yw\ZC\
stages of a design. |KU>+4=
@
A rule of thumb for the first Monte Carlo run on a system is to have an average of at *M+:GH/5
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays 9S&6 u1
on the receiver to achieve uniform distribution. It is likely that you will need to MZ+8wr/y
define more rays than 800 in a simulation in order to get 800 rays on the receiver. Kj}hb)HU
When using simplified meshes as merit functions, you should check the before and IH[/fd0
after performance of a design to verify that the changes correlate to the changes of 8d I gw
the merit function during optimization. As a design reaches its final performance AZl|;
y
level, you will have to add rays to the simulation to reduce the noise floor so that pK *-In
sufficient accuracy and mesh resolution are available for the optimizer to find the JYm@Llf)$
best solution.
