How Many Rays Do I Need for Monte Carlo Optimization? 'zE:
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While it is important to ensure that a sufficient number of rays are traced to g .3 f2w
distinguish the merit function value from the noise floor, it is often not necessary to p#)e:/Qy
trace as many rays during optimization as you might to obtain a given level of tzZ|S<e6=\
accuracy for analysis purposes. What matters during optimization is that the tAaYL
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changes the optimizer makes to the model affect the merit function in the same way e "_&z#
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that the overall performance is affected. It is possible to define the merit function so W^w d
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that it has less accuracy and/or coarser mesh resolution than meshes used for ~M*7N@D
analysis and yet produce improvements during optimization, especially in the early #uB[&GG}W
stages of a design. q{/*n]K
A rule of thumb for the first Monte Carlo run on a system is to have an average of at gEE9/\>%-
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays u]R $]& <
on the receiver to achieve uniform distribution. It is likely that you will need to *}7U`Aa
define more rays than 800 in a simulation in order to get 800 rays on the receiver. O(odNQy~
When using simplified meshes as merit functions, you should check the before and q v.n9 9?]
after performance of a design to verify that the changes correlate to the changes of `nKJR'QC
the merit function during optimization. As a design reaches its final performance hRk,vB ]
level, you will have to add rays to the simulation to reduce the noise floor so that k/U>N|5
sufficient accuracy and mesh resolution are available for the optimizer to find the 2f `&WUe
best solution.
