How Many Rays Do I Need for Monte Carlo Optimization? Ch t%uzb,
While it is important to ensure that a sufficient number of rays are traced to JYQ.EAsr!
distinguish the merit function value from the noise floor, it is often not necessary to @`S.@^%7fO
trace as many rays during optimization as you might to obtain a given level of (n,N8k;
accuracy for analysis purposes. What matters during optimization is that the @y5= J`@=
changes the optimizer makes to the model affect the merit function in the same way _$5@uL{n"^
that the overall performance is affected. It is possible to define the merit function so F 5U|9<
that it has less accuracy and/or coarser mesh resolution than meshes used for I>aGp|4
analysis and yet produce improvements during optimization, especially in the early
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stages of a design. Z%OW5]q
A rule of thumb for the first Monte Carlo run on a system is to have an average of at e^8BV;+c
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays ke\[wa_!6b
on the receiver to achieve uniform distribution. It is likely that you will need to 7E\g
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define more rays than 800 in a simulation in order to get 800 rays on the receiver. 4gb'7'
When using simplified meshes as merit functions, you should check the before and cJ2PI
after performance of a design to verify that the changes correlate to the changes of (0R2T"/
the merit function during optimization. As a design reaches its final performance +(&| u q^
level, you will have to add rays to the simulation to reduce the noise floor so that l|q%% W0
sufficient accuracy and mesh resolution are available for the optimizer to find the r**f,PDZ
best solution.
