How Many Rays Do I Need for Monte Carlo Optimization? (9Of,2]&E
While it is important to ensure that a sufficient number of rays are traced to ] @uuB\u
distinguish the merit function value from the noise floor, it is often not necessary to 2 QgD<
trace as many rays during optimization as you might to obtain a given level of ;;K
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accuracy for analysis purposes. What matters during optimization is that the /RI"a^&9A
changes the optimizer makes to the model affect the merit function in the same way hr W2#v
that the overall performance is affected. It is possible to define the merit function so @xeJ$
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that it has less accuracy and/or coarser mesh resolution than meshes used for ]oLyvG
analysis and yet produce improvements during optimization, especially in the early 5><T#0W?
stages of a design. 9y*2AaxW
A rule of thumb for the first Monte Carlo run on a system is to have an average of at 8 GeJ%^0o}
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays mLfY^&2Pr
on the receiver to achieve uniform distribution. It is likely that you will need to w!jY(W K U
define more rays than 800 in a simulation in order to get 800 rays on the receiver. b(\Mi_J
When using simplified meshes as merit functions, you should check the before and !j/54,
after performance of a design to verify that the changes correlate to the changes of $;rvKco)%
the merit function during optimization. As a design reaches its final performance {_4`0J`3
level, you will have to add rays to the simulation to reduce the noise floor so that 0ev='v8 ?
sufficient accuracy and mesh resolution are available for the optimizer to find the [r1dgwh 8
best solution.
