How Many Rays Do I Need for Monte Carlo Optimization? /)Sw QgK#
While it is important to ensure that a sufficient number of rays are traced to r)<]W@ Pr
distinguish the merit function value from the noise floor, it is often not necessary to C~vU
trace as many rays during optimization as you might to obtain a given level of >9 W ;u`
accuracy for analysis purposes. What matters during optimization is that the UYH;15s
changes the optimizer makes to the model affect the merit function in the same way S .rT5A[
that the overall performance is affected. It is possible to define the merit function so *=L3bBu?
that it has less accuracy and/or coarser mesh resolution than meshes used for yy4Q Y%
analysis and yet produce improvements during optimization, especially in the early SoODss~X
stages of a design. u~yJFI o
A rule of thumb for the first Monte Carlo run on a system is to have an average of at <ns[(
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least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays 4KE"r F
on the receiver to achieve uniform distribution. It is likely that you will need to 2 q J}5
define more rays than 800 in a simulation in order to get 800 rays on the receiver. Q7$ILW-S
When using simplified meshes as merit functions, you should check the before and buGW+Tr WY
after performance of a design to verify that the changes correlate to the changes of F\+wM*:U
the merit function during optimization. As a design reaches its final performance hS&,Gm`^
level, you will have to add rays to the simulation to reduce the noise floor so that bD<[OerG
sufficient accuracy and mesh resolution are available for the optimizer to find the fGJ PZe
best solution.
