How Many Rays Do I Need for Monte Carlo Optimization? mQ9y{}t=4
While it is important to ensure that a sufficient number of rays are traced to
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distinguish the merit function value from the noise floor, it is often not necessary to g 'td(i[
trace as many rays during optimization as you might to obtain a given level of U ]W "
accuracy for analysis purposes. What matters during optimization is that the }USOWsLSt
changes the optimizer makes to the model affect the merit function in the same way MDZPp;\)
that the overall performance is affected. It is possible to define the merit function so KGG nypx`
that it has less accuracy and/or coarser mesh resolution than meshes used for Uz=o l.E
analysis and yet produce improvements during optimization, especially in the early rk47 $36X
stages of a design. Nza@6nI"
A rule of thumb for the first Monte Carlo run on a system is to have an average of at c axOxRo\
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays {Iz"]W h<f
on the receiver to achieve uniform distribution. It is likely that you will need to 4+89 M
define more rays than 800 in a simulation in order to get 800 rays on the receiver. _gEojuaN
When using simplified meshes as merit functions, you should check the before and $Wjx$fD
after performance of a design to verify that the changes correlate to the changes of C~WWuju'
the merit function during optimization. As a design reaches its final performance n 5#QQk2
level, you will have to add rays to the simulation to reduce the noise floor so that 3a&HW
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sufficient accuracy and mesh resolution are available for the optimizer to find the T
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best solution.
