How Many Rays Do I Need for Monte Carlo Optimization? }#~E-N3x
While it is important to ensure that a sufficient number of rays are traced to a` 9pHH:7Q
distinguish the merit function value from the noise floor, it is often not necessary to z<P?p
trace as many rays during optimization as you might to obtain a given level of 67H?xsk@n
accuracy for analysis purposes. What matters during optimization is that the 9;n*u9<
changes the optimizer makes to the model affect the merit function in the same way Uv?^qe0=
that the overall performance is affected. It is possible to define the merit function so n}9<7e~ /
that it has less accuracy and/or coarser mesh resolution than meshes used for ZJFF4($qN
analysis and yet produce improvements during optimization, especially in the early aox@- jyr
stages of a design. :
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A rule of thumb for the first Monte Carlo run on a system is to have an average of at zZ Y1E @~
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays 1DU
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on the receiver to achieve uniform distribution. It is likely that you will need to nT~XctwF
define more rays than 800 in a simulation in order to get 800 rays on the receiver. %#EzZ D
When using simplified meshes as merit functions, you should check the before and 2u0B=0x
after performance of a design to verify that the changes correlate to the changes of [SKDsJRPP
the merit function during optimization. As a design reaches its final performance s LD Ea
level, you will have to add rays to the simulation to reduce the noise floor so that 04-@c
sufficient accuracy and mesh resolution are available for the optimizer to find the XdzC/ { G
best solution.
