How Many Rays Do I Need for Monte Carlo Optimization? X=OJgyO/
While it is important to ensure that a sufficient number of rays are traced to ) /<\|mR
distinguish the merit function value from the noise floor, it is often not necessary to * (@[E
trace as many rays during optimization as you might to obtain a given level of b<rJ@1qtJ
accuracy for analysis purposes. What matters during optimization is that the v:]
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changes the optimizer makes to the model affect the merit function in the same way %g&i.2v
that the overall performance is affected. It is possible to define the merit function so 80ms7 B
that it has less accuracy and/or coarser mesh resolution than meshes used for GwVSRI:[N
analysis and yet produce improvements during optimization, especially in the early C,m
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stages of a design. jG3i
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A rule of thumb for the first Monte Carlo run on a system is to have an average of at 7^:0? Q
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays Ijj]_V{,
on the receiver to achieve uniform distribution. It is likely that you will need to u
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define more rays than 800 in a simulation in order to get 800 rays on the receiver. [P_1a`b
When using simplified meshes as merit functions, you should check the before and 7[ra#>e8'
after performance of a design to verify that the changes correlate to the changes of 7e-l`]
the merit function during optimization. As a design reaches its final performance y/@.T\p
level, you will have to add rays to the simulation to reduce the noise floor so that d6k`=Hlg
sufficient accuracy and mesh resolution are available for the optimizer to find the
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best solution.
