How Many Rays Do I Need for Monte Carlo Optimization? exq5Z c%
While it is important to ensure that a sufficient number of rays are traced to \3hA_{ w
distinguish the merit function value from the noise floor, it is often not necessary to !( lcUdBd
trace as many rays during optimization as you might to obtain a given level of SnE^\I^O
accuracy for analysis purposes. What matters during optimization is that the i~h@}0WR"
changes the optimizer makes to the model affect the merit function in the same way <Y k i8
that the overall performance is affected. It is possible to define the merit function so X['9;1Xr
that it has less accuracy and/or coarser mesh resolution than meshes used for 1AAyzAP9`
analysis and yet produce improvements during optimization, especially in the early r
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stages of a design. 4&W?: =H2
A rule of thumb for the first Monte Carlo run on a system is to have an average of at Au,oX2$
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays ]k!Xb
on the receiver to achieve uniform distribution. It is likely that you will need to ^+x?@$rq
define more rays than 800 in a simulation in order to get 800 rays on the receiver. Et3 I(X 3
When using simplified meshes as merit functions, you should check the before and Cd*h4Q]S
after performance of a design to verify that the changes correlate to the changes of c)#P} Ai
the merit function during optimization. As a design reaches its final performance = TD`P et
level, you will have to add rays to the simulation to reduce the noise floor so that O c L7] b0
sufficient accuracy and mesh resolution are available for the optimizer to find the uzdPA'u
best solution.
