How Many Rays Do I Need for Monte Carlo Optimization? Oe uM9c{
While it is important to ensure that a sufficient number of rays are traced to dT%$"sj5
distinguish the merit function value from the noise floor, it is often not necessary to $EB&]t+
trace as many rays during optimization as you might to obtain a given level of ]i-peBxw
accuracy for analysis purposes. What matters during optimization is that the wW~y?A"{2
changes the optimizer makes to the model affect the merit function in the same way }jQxw i)
that the overall performance is affected. It is possible to define the merit function so ,{HxX0
that it has less accuracy and/or coarser mesh resolution than meshes used for 0Jh^((i*
analysis and yet produce improvements during optimization, especially in the early ' {L5 3cH=
stages of a design. g{zvks~it
A rule of thumb for the first Monte Carlo run on a system is to have an average of at 9U_uw
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least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays S0\;FmLIc
on the receiver to achieve uniform distribution. It is likely that you will need to @{_L38. Nw
define more rays than 800 in a simulation in order to get 800 rays on the receiver. ) ")_aA
When using simplified meshes as merit functions, you should check the before and ^
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after performance of a design to verify that the changes correlate to the changes of I6 x
the merit function during optimization. As a design reaches its final performance |&+0Tg~ZE
level, you will have to add rays to the simulation to reduce the noise floor so that ,m-z D
sufficient accuracy and mesh resolution are available for the optimizer to find the iyF ~:[8
best solution.
