How Many Rays Do I Need for Monte Carlo Optimization? 9f_Q s4
While it is important to ensure that a sufficient number of rays are traced to X}G3>HcP
distinguish the merit function value from the noise floor, it is often not necessary to 8;Eg>_cL:
trace as many rays during optimization as you might to obtain a given level of \>p\~[cxt
accuracy for analysis purposes. What matters during optimization is that the Zil<*(kv{
changes the optimizer makes to the model affect the merit function in the same way BfdS3VrZ/
that the overall performance is affected. It is possible to define the merit function so GRj#1OqL
that it has less accuracy and/or coarser mesh resolution than meshes used for }-2U,X g[
analysis and yet produce improvements during optimization, especially in the early pu,|_N[xq8
stages of a design. +puF0]TR,i
A rule of thumb for the first Monte Carlo run on a system is to have an average of at B' !I{LC
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays ]D&\|,,(
on the receiver to achieve uniform distribution. It is likely that you will need to .B rYz:#A
define more rays than 800 in a simulation in order to get 800 rays on the receiver. ;QqC c !b
When using simplified meshes as merit functions, you should check the before and #[ yZP9
after performance of a design to verify that the changes correlate to the changes of ^Q5advxuq
the merit function during optimization. As a design reaches its final performance $
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level, you will have to add rays to the simulation to reduce the noise floor so that WI \jm&H r
sufficient accuracy and mesh resolution are available for the optimizer to find the NZ:KJ8ea"
best solution.
