How Many Rays Do I Need for Monte Carlo Optimization? ;NvhL|R
While it is important to ensure that a sufficient number of rays are traced to {Hrr :h C
distinguish the merit function value from the noise floor, it is often not necessary to 'Gm!Jblo@
trace as many rays during optimization as you might to obtain a given level of m -&a~l
accuracy for analysis purposes. What matters during optimization is that the r;5 AY
changes the optimizer makes to the model affect the merit function in the same way r&LCoe'\{i
that the overall performance is affected. It is possible to define the merit function so qrORP3D@
that it has less accuracy and/or coarser mesh resolution than meshes used for w|3fioLs
analysis and yet produce improvements during optimization, especially in the early -f
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stages of a design. rK0|9^i{
A rule of thumb for the first Monte Carlo run on a system is to have an average of at wE.@0
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays `zV -1)=
on the receiver to achieve uniform distribution. It is likely that you will need to u8$~N$L
define more rays than 800 in a simulation in order to get 800 rays on the receiver. k -t, y|N
When using simplified meshes as merit functions, you should check the before and $[L)f|
l
after performance of a design to verify that the changes correlate to the changes of N-_| %C-.
the merit function during optimization. As a design reaches its final performance 9h)P8B. >M
level, you will have to add rays to the simulation to reduce the noise floor so that y D=)&->Ra
sufficient accuracy and mesh resolution are available for the optimizer to find the ) G F
best solution.
