How Many Rays Do I Need for Monte Carlo Optimization? ;Cqjg.wkB
While it is important to ensure that a sufficient number of rays are traced to W&(98}oT
distinguish the merit function value from the noise floor, it is often not necessary to K=C) .5=U
trace as many rays during optimization as you might to obtain a given level of -, #LTW<.
accuracy for analysis purposes. What matters during optimization is that the h 66X746
changes the optimizer makes to the model affect the merit function in the same way 9ApGn!`
that the overall performance is affected. It is possible to define the merit function so 0^{?kg2o_
that it has less accuracy and/or coarser mesh resolution than meshes used for XZ8;Ow=
analysis and yet produce improvements during optimization, especially in the early VpMPTEZ*L
stages of a design. zE NlL
A rule of thumb for the first Monte Carlo run on a system is to have an average of at ePJtdKN:
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays :-kXZe
on the receiver to achieve uniform distribution. It is likely that you will need to c;e2=
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define more rays than 800 in a simulation in order to get 800 rays on the receiver. #%i-{t+_>
When using simplified meshes as merit functions, you should check the before and #GHL F
after performance of a design to verify that the changes correlate to the changes of nu#_,x<LS
the merit function during optimization. As a design reaches its final performance #D{//P|;
level, you will have to add rays to the simulation to reduce the noise floor so that S oa5TM
sufficient accuracy and mesh resolution are available for the optimizer to find the <KDl2>O
best solution.
