How Many Rays Do I Need for Monte Carlo Optimization? BllDWKb
While it is important to ensure that a sufficient number of rays are traced to ry z /rf
distinguish the merit function value from the noise floor, it is often not necessary to 4p*?7g_WVH
trace as many rays during optimization as you might to obtain a given level of a "MTQFm'
accuracy for analysis purposes. What matters during optimization is that the 1w(<0Be
changes the optimizer makes to the model affect the merit function in the same way cF-Jc}h
that the overall performance is affected. It is possible to define the merit function so >9< _s
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that it has less accuracy and/or coarser mesh resolution than meshes used for LqMe'z
analysis and yet produce improvements during optimization, especially in the early
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stages of a design. U$MWsDn
A rule of thumb for the first Monte Carlo run on a system is to have an average of at B'NS&7+].
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays 4u7c7K>\Y
on the receiver to achieve uniform distribution. It is likely that you will need to p!. /
define more rays than 800 in a simulation in order to get 800 rays on the receiver. W^-hMT]uD
When using simplified meshes as merit functions, you should check the before and Jv -zB]3 &
after performance of a design to verify that the changes correlate to the changes of JkRG t Yq
the merit function during optimization. As a design reaches its final performance &3!i@2d;3f
level, you will have to add rays to the simulation to reduce the noise floor so that n5/Z Jur
sufficient accuracy and mesh resolution are available for the optimizer to find the DX]z=d)tc
best solution.
