How Many Rays Do I Need for Monte Carlo Optimization? _'W en
While it is important to ensure that a sufficient number of rays are traced to F5h OKUjv
distinguish the merit function value from the noise floor, it is often not necessary to Dd 3GdG@*~
trace as many rays during optimization as you might to obtain a given level of {Q% "{h']
accuracy for analysis purposes. What matters during optimization is that the _iJ8*v 8 A
changes the optimizer makes to the model affect the merit function in the same way \Ax[/J2aO
that the overall performance is affected. It is possible to define the merit function so mbij& 0
that it has less accuracy and/or coarser mesh resolution than meshes used for Lrr 1) h
analysis and yet produce improvements during optimization, especially in the early %u t^ O
stages of a design. 9 kpCn.rJ
A rule of thumb for the first Monte Carlo run on a system is to have an average of at #RJFJb/
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays % yVboA1
on the receiver to achieve uniform distribution. It is likely that you will need to u?ALZxj?
define more rays than 800 in a simulation in order to get 800 rays on the receiver. 5Tl3k=o}
When using simplified meshes as merit functions, you should check the before and f
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after performance of a design to verify that the changes correlate to the changes of ckglDhC
the merit function during optimization. As a design reaches its final performance LD.^.4{c:
level, you will have to add rays to the simulation to reduce the noise floor so that p $qpC$F
sufficient accuracy and mesh resolution are available for the optimizer to find the >+9f{FP
9
best solution.
