How Many Rays Do I Need for Monte Carlo Optimization?
]zj&U#{ While it is important to ensure that a sufficient number of rays are traced to
:T>OJ"p distinguish the merit function value from the noise floor, it is often not necessary to
n<@C'\j@ trace as many rays during optimization as you might to obtain a given level of
+QOK]NJN accuracy for analysis purposes. What matters during optimization is that the
IL uQ f- changes the optimizer makes to the model affect the merit function in the same way
~ 588m d : that the overall performance is affected. It is possible to define the merit function so
-G#m'W& that it has less accuracy and/or coarser mesh resolution than meshes used for
<]_[o:nOP analysis and yet produce improvements during optimization, especially in the early
snNB;hkj stages of a design.
z5D*UOy5M A rule of thumb for the first Monte Carlo run on a system is to have an average of at
J l{My^I5 least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays
-s7!:MB%g on the receiver to achieve uniform distribution. It is likely that you will need to
#;+S AoN
define more rays than 800 in a simulation in order to get 800 rays on the receiver.
?5^DQ|Hg ^ When using simplified meshes as merit functions, you should check the before and
($8!r|g5# after performance of a design to verify that the changes correlate to the changes of
)T&r770 the merit function during optimization. As a design reaches its final performance
J/,m'wH level, you will have to add rays to the simulation to reduce the noise floor so that
I47s q
