How Many Rays Do I Need for Monte Carlo Optimization? 7yx$N n`(
While it is important to ensure that a sufficient number of rays are traced to 5i-Rgl o
distinguish the merit function value from the noise floor, it is often not necessary to TU GNq
trace as many rays during optimization as you might to obtain a given level of ]b= P=
accuracy for analysis purposes. What matters during optimization is that the Q\WC+,_%
changes the optimizer makes to the model affect the merit function in the same way ~{D[
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that the overall performance is affected. It is possible to define the merit function so Ga^Zb^y
that it has less accuracy and/or coarser mesh resolution than meshes used for 1|?05< 8
analysis and yet produce improvements during optimization, especially in the early 0k'e:AjP
stages of a design. (C\hVy2X?N
A rule of thumb for the first Monte Carlo run on a system is to have an average of at {XW Z<OjG
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays -YjA+XP
on the receiver to achieve uniform distribution. It is likely that you will need to }?@rO`:EF+
define more rays than 800 in a simulation in order to get 800 rays on the receiver. :mS# h@l
When using simplified meshes as merit functions, you should check the before and x+h~gckLb
after performance of a design to verify that the changes correlate to the changes of tU^kQR!
the merit function during optimization. As a design reaches its final performance <d<mvXbw_@
level, you will have to add rays to the simulation to reduce the noise floor so that #Cn Hf
sufficient accuracy and mesh resolution are available for the optimizer to find the @_"9D y Y%
best solution.
