How Many Rays Do I Need for Monte Carlo Optimization? \k;*Ej~.
While it is important to ensure that a sufficient number of rays are traced to [C.Pzo
distinguish the merit function value from the noise floor, it is often not necessary to tFO86 !ln
trace as many rays during optimization as you might to obtain a given level of Sc`W'q^X
accuracy for analysis purposes. What matters during optimization is that the 1s "6
changes the optimizer makes to the model affect the merit function in the same way 2y`rS
_2
that the overall performance is affected. It is possible to define the merit function so /2tgxm$}
that it has less accuracy and/or coarser mesh resolution than meshes used for T\NvN&h-
analysis and yet produce improvements during optimization, especially in the early $x)C_WZj?
stages of a design. s: ~3|D][
A rule of thumb for the first Monte Carlo run on a system is to have an average of at now\-XrS
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays m?`U;R[
on the receiver to achieve uniform distribution. It is likely that you will need to L?23Av0W
define more rays than 800 in a simulation in order to get 800 rays on the receiver. %n SLe~b
When using simplified meshes as merit functions, you should check the before and YP5V~-O/
after performance of a design to verify that the changes correlate to the changes of gR
)xw) !
the merit function during optimization. As a design reaches its final performance 37Q9goMov
level, you will have to add rays to the simulation to reduce the noise floor so that %lF}!
sufficient accuracy and mesh resolution are available for the optimizer to find the 4U( W~O
best solution.
