How Many Rays Do I Need for Monte Carlo Optimization? XM f>B|
While it is important to ensure that a sufficient number of rays are traced to TXT!Ae
distinguish the merit function value from the noise floor, it is often not necessary to qC6@
trace as many rays during optimization as you might to obtain a given level of qh|fq
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accuracy for analysis purposes. What matters during optimization is that the J\Db8O-/x4
changes the optimizer makes to the model affect the merit function in the same way K;7ea47m N
that the overall performance is affected. It is possible to define the merit function so i&KBMx
that it has less accuracy and/or coarser mesh resolution than meshes used for Dy &{PeE!
analysis and yet produce improvements during optimization, especially in the early &$bcB]C\3
stages of a design. KwNOB _
A rule of thumb for the first Monte Carlo run on a system is to have an average of at > -,$
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays h0] bIT{
on the receiver to achieve uniform distribution. It is likely that you will need to [gGo^^aW#
define more rays than 800 in a simulation in order to get 800 rays on the receiver. (QTQxZ
When using simplified meshes as merit functions, you should check the before and l6-
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after performance of a design to verify that the changes correlate to the changes of ~p?D[]h
the merit function during optimization. As a design reaches its final performance .On3ZN
level, you will have to add rays to the simulation to reduce the noise floor so that !Qq~lAJO;
sufficient accuracy and mesh resolution are available for the optimizer to find the Q[c:A@oW
best solution.
