How Many Rays Do I Need for Monte Carlo Optimization? |4` ;G(t a
While it is important to ensure that a sufficient number of rays are traced to 'm/`= QX
distinguish the merit function value from the noise floor, it is often not necessary to =}F$r5]
trace as many rays during optimization as you might to obtain a given level of K#y CZ2
accuracy for analysis purposes. What matters during optimization is that the HLq2a vs\
changes the optimizer makes to the model affect the merit function in the same way S9qc34\^=
that the overall performance is affected. It is possible to define the merit function so `2HNQiK'@
that it has less accuracy and/or coarser mesh resolution than meshes used for 8ROZ]Xh,x
analysis and yet produce improvements during optimization, especially in the early _o>?\ :A
stages of a design. i/,IG+4vI
A rule of thumb for the first Monte Carlo run on a system is to have an average of at ;PMy9 H
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays Y}r UVn
on the receiver to achieve uniform distribution. It is likely that you will need to &>}f\ch/
define more rays than 800 in a simulation in order to get 800 rays on the receiver. cA!o
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When using simplified meshes as merit functions, you should check the before and i | *r/
after performance of a design to verify that the changes correlate to the changes of -}H
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the merit function during optimization. As a design reaches its final performance M-C> I;a
level, you will have to add rays to the simulation to reduce the noise floor so that -{$L`{|G
sufficient accuracy and mesh resolution are available for the optimizer to find the qa?0GTA S
best solution.
