How Many Rays Do I Need for Monte Carlo Optimization? Y KWts y
While it is important to ensure that a sufficient number of rays are traced to pJ ;4rrSK
distinguish the merit function value from the noise floor, it is often not necessary to .GH#`j
trace as many rays during optimization as you might to obtain a given level of -/z #?J\
accuracy for analysis purposes. What matters during optimization is that the _|qs-USA
changes the optimizer makes to the model affect the merit function in the same way wrmbO T
that the overall performance is affected. It is possible to define the merit function so ? > 7SZiC`
that it has less accuracy and/or coarser mesh resolution than meshes used for ,a1
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analysis and yet produce improvements during optimization, especially in the early (TQhO$,
stages of a design. y4Fuh nb>
A rule of thumb for the first Monte Carlo run on a system is to have an average of at *^_ywqp
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays ];VJ 54
on the receiver to achieve uniform distribution. It is likely that you will need to "2a&G3}t"
define more rays than 800 in a simulation in order to get 800 rays on the receiver. v#WD$9QWs
When using simplified meshes as merit functions, you should check the before and C0. bjFT|
after performance of a design to verify that the changes correlate to the changes of QXg9ah~
the merit function during optimization. As a design reaches its final performance LYvjqNC&4
level, you will have to add rays to the simulation to reduce the noise floor so that $`O%bsjX
sufficient accuracy and mesh resolution are available for the optimizer to find the E,g5[s@
best solution.
