How Many Rays Do I Need for Monte Carlo Optimization? XRQ1Uh6
While it is important to ensure that a sufficient number of rays are traced to i%<NKE;v7m
distinguish the merit function value from the noise floor, it is often not necessary to ;/wH/!b
trace as many rays during optimization as you might to obtain a given level of TB&IB:4)R
accuracy for analysis purposes. What matters during optimization is that the 2vG
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changes the optimizer makes to the model affect the merit function in the same way !s/qqq:g
that the overall performance is affected. It is possible to define the merit function so 'q~<ZO
that it has less accuracy and/or coarser mesh resolution than meshes used for "K9[P :nw
analysis and yet produce improvements during optimization, especially in the early jck(cc= R
stages of a design. u*5}c7)uId
A rule of thumb for the first Monte Carlo run on a system is to have an average of at -:'%YHxX
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays Hf1b&8&:K
on the receiver to achieve uniform distribution. It is likely that you will need to I9aiAD0s
define more rays than 800 in a simulation in order to get 800 rays on the receiver. sKKc_H3YSH
When using simplified meshes as merit functions, you should check the before and 3WwCo.q;m
after performance of a design to verify that the changes correlate to the changes of d/Wp>A@dob
the merit function during optimization. As a design reaches its final performance `EvO^L
level, you will have to add rays to the simulation to reduce the noise floor so that =L1%gQJJ&
sufficient accuracy and mesh resolution are available for the optimizer to find the %(6+{'j~#
best solution.
