How Many Rays Do I Need for Monte Carlo Optimization?
jq-_4}w?C While it is important to ensure that a sufficient number of rays are traced to
59-c<I/}f distinguish the merit function value from the noise floor, it is often not necessary to
L4f3X~8,b trace as many rays during optimization as you might to obtain a given level of
R GX=) accuracy for analysis purposes. What matters during optimization is that the
cS+>J@L changes the optimizer makes to the model affect the merit function in the same way
yppo6 HGD that the overall performance is affected. It is possible to define the merit function so
k+4#!.HX^ that it has less accuracy and/or coarser mesh resolution than meshes used for
u2[w# analysis and yet produce improvements during optimization, especially in the early
s<o7!!c stages of a design.
|)G<,FJQE_ A rule of thumb for the first Monte Carlo run on a system is to have an average of at
R rgGEx least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays
w*MpX
U< on the receiver to achieve uniform distribution. It is likely that you will need to
G#1GXFDO{ define more rays than 800 in a simulation in order to get 800 rays on the receiver.
s9d_GhT%- When using simplified meshes as merit functions, you should check the before and
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