How Many Rays Do I Need for Monte Carlo Optimization? 2tf6GX :
While it is important to ensure that a sufficient number of rays are traced to Sl>>SP
distinguish the merit function value from the noise floor, it is often not necessary to c
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trace as many rays during optimization as you might to obtain a given level of -72j:nk
accuracy for analysis purposes. What matters during optimization is that the J &{xP8uq_
changes the optimizer makes to the model affect the merit function in the same way J>%t<xYf4
that the overall performance is affected. It is possible to define the merit function so @]=f?+y[ 2
that it has less accuracy and/or coarser mesh resolution than meshes used for V7C1FV2
analysis and yet produce improvements during optimization, especially in the early rl?7W];
stages of a design. M4?8xuC
A rule of thumb for the first Monte Carlo run on a system is to have an average of at Jq
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least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays `G?qY8
on the receiver to achieve uniform distribution. It is likely that you will need to .G CR!V
define more rays than 800 in a simulation in order to get 800 rays on the receiver. 2+ '| kt2
When using simplified meshes as merit functions, you should check the before and &g0g]G21*I
after performance of a design to verify that the changes correlate to the changes of yN\e{;z`
the merit function during optimization. As a design reaches its final performance -;pOh;W G
level, you will have to add rays to the simulation to reduce the noise floor so that #s2B%X
sufficient accuracy and mesh resolution are available for the optimizer to find the I4D<WoU;dJ
best solution.
