How Many Rays Do I Need for Monte Carlo Optimization? /`Y HPeXu
While it is important to ensure that a sufficient number of rays are traced to ad). X:Qs
distinguish the merit function value from the noise floor, it is often not necessary to tl |Q w";I
trace as many rays during optimization as you might to obtain a given level of "pb,|U
accuracy for analysis purposes. What matters during optimization is that the xyK_1n@b
changes the optimizer makes to the model affect the merit function in the same way je6H}eWTC6
that the overall performance is affected. It is possible to define the merit function so t = ErJ
that it has less accuracy and/or coarser mesh resolution than meshes used for :zk69P3
analysis and yet produce improvements during optimization, especially in the early t1,sG8Z
stages of a design. uUXvBA?l
A rule of thumb for the first Monte Carlo run on a system is to have an average of at u:r'&#jb~@
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays kK]JN
on the receiver to achieve uniform distribution. It is likely that you will need to a)'^'jm)4
define more rays than 800 in a simulation in order to get 800 rays on the receiver. %UuV^C
When using simplified meshes as merit functions, you should check the before and w:l/B
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after performance of a design to verify that the changes correlate to the changes of -AUdBG
the merit function during optimization. As a design reaches its final performance ?Xscc mN
level, you will have to add rays to the simulation to reduce the noise floor so that #F\}PCBe'
sufficient accuracy and mesh resolution are available for the optimizer to find the Iy\{)+}aS
best solution.
