How Many Rays Do I Need for Monte Carlo Optimization? B,\VLX
While it is important to ensure that a sufficient number of rays are traced to
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distinguish the merit function value from the noise floor, it is often not necessary to ol"|?*3q
trace as many rays during optimization as you might to obtain a given level of G{!er:Vwdh
accuracy for analysis purposes. What matters during optimization is that the ]P3m=/w
changes the optimizer makes to the model affect the merit function in the same way M m$\j*f/
that the overall performance is affected. It is possible to define the merit function so {]+ t<
that it has less accuracy and/or coarser mesh resolution than meshes used for v\,N"X(,
analysis and yet produce improvements during optimization, especially in the early 1_TuA(
stages of a design. >>J3"XHX
A rule of thumb for the first Monte Carlo run on a system is to have an average of at wNHn.
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays tQ{/9bN?P
on the receiver to achieve uniform distribution. It is likely that you will need to bvtpqI QZ
define more rays than 800 in a simulation in order to get 800 rays on the receiver. o=YOn&@%
When using simplified meshes as merit functions, you should check the before and \Sd8PGl*'
after performance of a design to verify that the changes correlate to the changes of nq{/fD(2
the merit function during optimization. As a design reaches its final performance L"&T3i
level, you will have to add rays to the simulation to reduce the noise floor so that Kd- 1EU
sufficient accuracy and mesh resolution are available for the optimizer to find the 7Jlkn=9e:
best solution.
