How Many Rays Do I Need for Monte Carlo Optimization? ?y|&Mz'XJ(
While it is important to ensure that a sufficient number of rays are traced to RFw0u 0Nrz
distinguish the merit function value from the noise floor, it is often not necessary to AO<T6 VK
trace as many rays during optimization as you might to obtain a given level of N[@~q~v
accuracy for analysis purposes. What matters during optimization is that the DY `0 `T
changes the optimizer makes to the model affect the merit function in the same way U&"L9o`2
that the overall performance is affected. It is possible to define the merit function so +v/y{8Fu
that it has less accuracy and/or coarser mesh resolution than meshes used for
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analysis and yet produce improvements during optimization, especially in the early |QS|\8g{0V
stages of a design. $NCvF'
A rule of thumb for the first Monte Carlo run on a system is to have an average of at f@sC~A. 9\
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays q}i#XQ U
on the receiver to achieve uniform distribution. It is likely that you will need to ?g1eW q&
define more rays than 800 in a simulation in order to get 800 rays on the receiver. \BBs;z[/
When using simplified meshes as merit functions, you should check the before and Y6wr}U
after performance of a design to verify that the changes correlate to the changes of % LnLB
the merit function during optimization. As a design reaches its final performance 'e:4
level, you will have to add rays to the simulation to reduce the noise floor so that }w)}=WmD
sufficient accuracy and mesh resolution are available for the optimizer to find the KXMf2)p a
best solution.
