How Many Rays Do I Need for Monte Carlo Optimization? k9iB-=X?4s
While it is important to ensure that a sufficient number of rays are traced to &Tk@2<5=
distinguish the merit function value from the noise floor, it is often not necessary to 6<9gVh<=w
trace as many rays during optimization as you might to obtain a given level of C^ Oy.s
accuracy for analysis purposes. What matters during optimization is that the =7 -@&S=?s
changes the optimizer makes to the model affect the merit function in the same way YT)@&HaF
that the overall performance is affected. It is possible to define the merit function so ?xtP\~
that it has less accuracy and/or coarser mesh resolution than meshes used for |%fM*F^7/
analysis and yet produce improvements during optimization, especially in the early wE#z)2?`\
stages of a design. S3?U-R^`
A rule of thumb for the first Monte Carlo run on a system is to have an average of at qfyuq]
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays #80M+m
on the receiver to achieve uniform distribution. It is likely that you will need to
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define more rays than 800 in a simulation in order to get 800 rays on the receiver. _[ S<Cb*1
When using simplified meshes as merit functions, you should check the before and DQ= /Jr~
after performance of a design to verify that the changes correlate to the changes of myDcr|j-a
the merit function during optimization. As a design reaches its final performance zE]h] $oi
level, you will have to add rays to the simulation to reduce the noise floor so that 7aeyddpM
sufficient accuracy and mesh resolution are available for the optimizer to find the =yLJGNK[
best solution.
