How Many Rays Do I Need for Monte Carlo Optimization? 'E6 gEJ
While it is important to ensure that a sufficient number of rays are traced to 7n3x19T
distinguish the merit function value from the noise floor, it is often not necessary to :."n@sA@
trace as many rays during optimization as you might to obtain a given level of H9a3 rA>
accuracy for analysis purposes. What matters during optimization is that the aN); P>
changes the optimizer makes to the model affect the merit function in the same way d)J] Y=j
that the overall performance is affected. It is possible to define the merit function so 9p0HFri[
that it has less accuracy and/or coarser mesh resolution than meshes used for onypwfIk)t
analysis and yet produce improvements during optimization, especially in the early ObHz+qRG
stages of a design. 07WIa@Q
A rule of thumb for the first Monte Carlo run on a system is to have an average of at {bsr
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least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays WaZ@
on the receiver to achieve uniform distribution. It is likely that you will need to x_^OS"h-
define more rays than 800 in a simulation in order to get 800 rays on the receiver. UOL%tT
When using simplified meshes as merit functions, you should check the before and *ytd.^@r
after performance of a design to verify that the changes correlate to the changes of U\!LZ?gC
the merit function during optimization. As a design reaches its final performance kj YO0!C
level, you will have to add rays to the simulation to reduce the noise floor so that #__'U6`(
sufficient accuracy and mesh resolution are available for the optimizer to find the
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best solution.
