How Many Rays Do I Need for Monte Carlo Optimization? TeHR, GB
While it is important to ensure that a sufficient number of rays are traced to 09r.0K s
distinguish the merit function value from the noise floor, it is often not necessary to ~ ~&M&Fe
trace as many rays during optimization as you might to obtain a given level of +u7mw<A
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accuracy for analysis purposes. What matters during optimization is that the &}<IR\ci
changes the optimizer makes to the model affect the merit function in the same way d4/ZOj+%
that the overall performance is affected. It is possible to define the merit function so 0oD? 4gn
that it has less accuracy and/or coarser mesh resolution than meshes used for se&:Y&vrc~
analysis and yet produce improvements during optimization, especially in the early o4xZaF4+
stages of a design. EyhQjs aT
A rule of thumb for the first Monte Carlo run on a system is to have an average of at P6 9S[aqW
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays `3~w#?+=*
on the receiver to achieve uniform distribution. It is likely that you will need to 'j|;M
define more rays than 800 in a simulation in order to get 800 rays on the receiver. x@yF|8
When using simplified meshes as merit functions, you should check the before and I / c*
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after performance of a design to verify that the changes correlate to the changes of =4G9ev
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the merit function during optimization. As a design reaches its final performance \%UA6uj
level, you will have to add rays to the simulation to reduce the noise floor so that " ~tEmMz
sufficient accuracy and mesh resolution are available for the optimizer to find the /p~gm\5Z
best solution.
