How Many Rays Do I Need for Monte Carlo Optimization? wlX
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While it is important to ensure that a sufficient number of rays are traced to apm,$Vvjy
distinguish the merit function value from the noise floor, it is often not necessary to < daBP[
trace as many rays during optimization as you might to obtain a given level of '^t(=02J
accuracy for analysis purposes. What matters during optimization is that the H,1 I z@W1
changes the optimizer makes to the model affect the merit function in the same way A%#."2vq~
that the overall performance is affected. It is possible to define the merit function so ~!dO2\X+
that it has less accuracy and/or coarser mesh resolution than meshes used for k:E+]5
analysis and yet produce improvements during optimization, especially in the early t9kqX(!
stages of a design. y=WCR*N
A rule of thumb for the first Monte Carlo run on a system is to have an average of at
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least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays 3rv~ r0
on the receiver to achieve uniform distribution. It is likely that you will need to cy_zEJjbD
define more rays than 800 in a simulation in order to get 800 rays on the receiver. *7/MeE6)i
When using simplified meshes as merit functions, you should check the before and [%7oq;^J
after performance of a design to verify that the changes correlate to the changes of `>$l2,
the merit function during optimization. As a design reaches its final performance {[FJkP2l
level, you will have to add rays to the simulation to reduce the noise floor so that 0bMbM^xV6
sufficient accuracy and mesh resolution are available for the optimizer to find the ZltY_5l
best solution.
