How Many Rays Do I Need for Monte Carlo Optimization? pb}4{]sI
While it is important to ensure that a sufficient number of rays are traced to }W$}blbp
distinguish the merit function value from the noise floor, it is often not necessary to Z$2Vd`XP
trace as many rays during optimization as you might to obtain a given level of ^5 ~)m6=2
accuracy for analysis purposes. What matters during optimization is that the 15wwu} X
changes the optimizer makes to the model affect the merit function in the same way kf2e-)uUs
that the overall performance is affected. It is possible to define the merit function so ?PDrj/: *
that it has less accuracy and/or coarser mesh resolution than meshes used for mBWhC<kK s
analysis and yet produce improvements during optimization, especially in the early p^i]{"sjbU
stages of a design. O`FuXB(t
A rule of thumb for the first Monte Carlo run on a system is to have an average of at i=j4Wg ,{J
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays <G #z;]N
on the receiver to achieve uniform distribution. It is likely that you will need to 73tWeZ8rvx
define more rays than 800 in a simulation in order to get 800 rays on the receiver. }I
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When using simplified meshes as merit functions, you should check the before and - K U@0G
after performance of a design to verify that the changes correlate to the changes of {p M 3f
the merit function during optimization. As a design reaches its final performance Cswa5 l`af
level, you will have to add rays to the simulation to reduce the noise floor so that egy# 8U)Z
sufficient accuracy and mesh resolution are available for the optimizer to find the ff<ad l-
best solution.
