How Many Rays Do I Need for Monte Carlo Optimization? #'RfwldD9
While it is important to ensure that a sufficient number of rays are traced to b !nA. `T
distinguish the merit function value from the noise floor, it is often not necessary to {BJH}vV1)
trace as many rays during optimization as you might to obtain a given level of $v"CQD
accuracy for analysis purposes. What matters during optimization is that the */$] kE
changes the optimizer makes to the model affect the merit function in the same way 5-S -r9
that the overall performance is affected. It is possible to define the merit function so {>TAnb?n
that it has less accuracy and/or coarser mesh resolution than meshes used for u^ x<xw6f
analysis and yet produce improvements during optimization, especially in the early 0}T 56aD=!
stages of a design. #] ^M/y
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A rule of thumb for the first Monte Carlo run on a system is to have an average of at O~6AX)|&=
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays h///
on the receiver to achieve uniform distribution. It is likely that you will need to 6{ fo.M?
define more rays than 800 in a simulation in order to get 800 rays on the receiver. (IA: 4E}
When using simplified meshes as merit functions, you should check the before and o_ [I#PT
after performance of a design to verify that the changes correlate to the changes of :r{W)(mm
the merit function during optimization. As a design reaches its final performance <xH!
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level, you will have to add rays to the simulation to reduce the noise floor so that vB5mOXGN q
sufficient accuracy and mesh resolution are available for the optimizer to find the
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best solution.
