How Many Rays Do I Need for Monte Carlo Optimization? <RmI)g>'_^
While it is important to ensure that a sufficient number of rays are traced to D?w?0b Eu
distinguish the merit function value from the noise floor, it is often not necessary to m*L*# ZBS
trace as many rays during optimization as you might to obtain a given level of XqVh C):
accuracy for analysis purposes. What matters during optimization is that the ^|@t 2Rp@
changes the optimizer makes to the model affect the merit function in the same way k
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that the overall performance is affected. It is possible to define the merit function so .+<Ul ]e/
that it has less accuracy and/or coarser mesh resolution than meshes used for iH& I zv
analysis and yet produce improvements during optimization, especially in the early <|~8Ezd
stages of a design. 4h >Dpml
A rule of thumb for the first Monte Carlo run on a system is to have an average of at bk E4{P"
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays + #gJ[Cc
on the receiver to achieve uniform distribution. It is likely that you will need to 4K82%P9a
define more rays than 800 in a simulation in order to get 800 rays on the receiver. B\a-Q,Wf
When using simplified meshes as merit functions, you should check the before and +tL]qO BP
after performance of a design to verify that the changes correlate to the changes of }
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the merit function during optimization. As a design reaches its final performance #C1u~db
level, you will have to add rays to the simulation to reduce the noise floor so that 8kQ
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sufficient accuracy and mesh resolution are available for the optimizer to find the /,'D4s:Gg
best solution.
