How Many Rays Do I Need for Monte Carlo Optimization? pFS
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While it is important to ensure that a sufficient number of rays are traced to ![qRoYpbg8
distinguish the merit function value from the noise floor, it is often not necessary to 4b] IazL)
trace as many rays during optimization as you might to obtain a given level of X"laZd947>
accuracy for analysis purposes. What matters during optimization is that the jg7d 7{{SB
changes the optimizer makes to the model affect the merit function in the same way g2!0 v B>
that the overall performance is affected. It is possible to define the merit function so NEZH<#
that it has less accuracy and/or coarser mesh resolution than meshes used for .Y+mwvLpRG
analysis and yet produce improvements during optimization, especially in the early _QD/!~O
stages of a design. 7^`RP e^a+
A rule of thumb for the first Monte Carlo run on a system is to have an average of at ;CLR{t(N#V
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays &f$a1#O}dx
on the receiver to achieve uniform distribution. It is likely that you will need to J!ln=h
define more rays than 800 in a simulation in order to get 800 rays on the receiver. 7 _X&5ni
When using simplified meshes as merit functions, you should check the before and 1{= E ?
after performance of a design to verify that the changes correlate to the changes of Y=PzN3
the merit function during optimization. As a design reaches its final performance cq- e
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level, you will have to add rays to the simulation to reduce the noise floor so that QxP` f KC8
sufficient accuracy and mesh resolution are available for the optimizer to find the \CP*i _:"
best solution.
