How Many Rays Do I Need for Monte Carlo Optimization? 8'Ie i78Ov
While it is important to ensure that a sufficient number of rays are traced to pwVaSnre`
distinguish the merit function value from the noise floor, it is often not necessary to ^g=j`f[ T
trace as many rays during optimization as you might to obtain a given level of D$Ao-6QE
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accuracy for analysis purposes. What matters during optimization is that the 1Q7]1 fRu
changes the optimizer makes to the model affect the merit function in the same way bh+m_$X~
that the overall performance is affected. It is possible to define the merit function so ojx2[a\
that it has less accuracy and/or coarser mesh resolution than meshes used for Prrz>
analysis and yet produce improvements during optimization, especially in the early s;#,c(
stages of a design. G%>{Z?!B
A rule of thumb for the first Monte Carlo run on a system is to have an average of at 'yR\%#s6
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays V6&6I
on the receiver to achieve uniform distribution. It is likely that you will need to < =!FB8 .
define more rays than 800 in a simulation in order to get 800 rays on the receiver. C,B{7s0-
When using simplified meshes as merit functions, you should check the before and A&{eC
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after performance of a design to verify that the changes correlate to the changes of A+Kp ECP
the merit function during optimization. As a design reaches its final performance $mxl&Qr>Q;
level, you will have to add rays to the simulation to reduce the noise floor so that gkDXt^O b
sufficient accuracy and mesh resolution are available for the optimizer to find the s=Xg6 D
best solution.
