How Many Rays Do I Need for Monte Carlo Optimization? %_{tzXim
While it is important to ensure that a sufficient number of rays are traced to *WIj4G.d
distinguish the merit function value from the noise floor, it is often not necessary to bLQ ^fH4ww
trace as many rays during optimization as you might to obtain a given level of 0 0SbH$SU
accuracy for analysis purposes. What matters during optimization is that the vt;{9\Y
changes the optimizer makes to the model affect the merit function in the same way f 3H ed
that the overall performance is affected. It is possible to define the merit function so L2pp6bW
that it has less accuracy and/or coarser mesh resolution than meshes used for _/s (7y!
analysis and yet produce improvements during optimization, especially in the early i 4%xfN
stages of a design.
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A rule of thumb for the first Monte Carlo run on a system is to have an average of at M\Z6$<H?U
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays T: IW%?M
on the receiver to achieve uniform distribution. It is likely that you will need to 1Lg-.-V
define more rays than 800 in a simulation in order to get 800 rays on the receiver. B,K>rCZ/
When using simplified meshes as merit functions, you should check the before and ;z IP,PMM
after performance of a design to verify that the changes correlate to the changes of @Q^P{
the merit function during optimization. As a design reaches its final performance USVqB\#
level, you will have to add rays to the simulation to reduce the noise floor so that W0k0$\iX
sufficient accuracy and mesh resolution are available for the optimizer to find the |d*&y#kV
best solution.
