How Many Rays Do I Need for Monte Carlo Optimization? *b6I%MZn
While it is important to ensure that a sufficient number of rays are traced to Xew1LPI
distinguish the merit function value from the noise floor, it is often not necessary to sx[&4 k[
trace as many rays during optimization as you might to obtain a given level of rt 3f7 s*
accuracy for analysis purposes. What matters during optimization is that the wzDk{4U
changes the optimizer makes to the model affect the merit function in the same way 20.-;jK
that the overall performance is affected. It is possible to define the merit function so :!+}XT7)/
that it has less accuracy and/or coarser mesh resolution than meshes used for } :RT,<
analysis and yet produce improvements during optimization, especially in the early EZ%w=
stages of a design. !e:i B7<
A rule of thumb for the first Monte Carlo run on a system is to have an average of at T<TcV9vM
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays OD?y
on the receiver to achieve uniform distribution. It is likely that you will need to .0Iun+nUD
define more rays than 800 in a simulation in order to get 800 rays on the receiver. ,TKs/-_?
When using simplified meshes as merit functions, you should check the before and AK@`'$
after performance of a design to verify that the changes correlate to the changes of RVgPH<1X@e
the merit function during optimization. As a design reaches its final performance LL= Z$U
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level, you will have to add rays to the simulation to reduce the noise floor so that |P ,zGy
sufficient accuracy and mesh resolution are available for the optimizer to find the m?D
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best solution.
