How Many Rays Do I Need for Monte Carlo Optimization? Qq.Ja%Zq
While it is important to ensure that a sufficient number of rays are traced to CA/Lv{[2
distinguish the merit function value from the noise floor, it is often not necessary to
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trace as many rays during optimization as you might to obtain a given level of -3y
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accuracy for analysis purposes. What matters during optimization is that the _ *t75e$-
changes the optimizer makes to the model affect the merit function in the same way 8)f/H &)>8
that the overall performance is affected. It is possible to define the merit function so m{yq.H[X
that it has less accuracy and/or coarser mesh resolution than meshes used for ,;h} <("q
analysis and yet produce improvements during optimization, especially in the early v+d`J55
stages of a design. V )oKsO
A rule of thumb for the first Monte Carlo run on a system is to have an average of at leXdxpc
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays `7V'A
on the receiver to achieve uniform distribution. It is likely that you will need to u@4khN:
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define more rays than 800 in a simulation in order to get 800 rays on the receiver. yyVE%e5nl
When using simplified meshes as merit functions, you should check the before and 7u%OYt
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after performance of a design to verify that the changes correlate to the changes of ^w
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the merit function during optimization. As a design reaches its final performance }hc+ENh
level, you will have to add rays to the simulation to reduce the noise floor so that (. $e@k=
sufficient accuracy and mesh resolution are available for the optimizer to find the cm>+f ^4?n
best solution.
