How Many Rays Do I Need for Monte Carlo Optimization? O | zLD
While it is important to ensure that a sufficient number of rays are traced to h&$,mbEoI
distinguish the merit function value from the noise floor, it is often not necessary to lM'yj}:~
trace as many rays during optimization as you might to obtain a given level of /'g"Ys?3
accuracy for analysis purposes. What matters during optimization is that the KXTx{R
changes the optimizer makes to the model affect the merit function in the same way z~+gche>
that the overall performance is affected. It is possible to define the merit function so I'%(f@u~
that it has less accuracy and/or coarser mesh resolution than meshes used for b1 NB:
analysis and yet produce improvements during optimization, especially in the early V-
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stages of a design. 'YUx&F cM
A rule of thumb for the first Monte Carlo run on a system is to have an average of at jtF et{
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays $bv l.c
on the receiver to achieve uniform distribution. It is likely that you will need to y/}ENUG R
define more rays than 800 in a simulation in order to get 800 rays on the receiver. u{"@
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When using simplified meshes as merit functions, you should check the before and #w:6<$
after performance of a design to verify that the changes correlate to the changes of l5bd);L tq
the merit function during optimization. As a design reaches its final performance YM EI
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level, you will have to add rays to the simulation to reduce the noise floor so that #m<<]L(o8W
sufficient accuracy and mesh resolution are available for the optimizer to find the 6a\YD{D] _
best solution.
