How Many Rays Do I Need for Monte Carlo Optimization? >BMJA:j
While it is important to ensure that a sufficient number of rays are traced to 6(B0gBCId
distinguish the merit function value from the noise floor, it is often not necessary to |OF<=G GO+
trace as many rays during optimization as you might to obtain a given level of aoz+g,1
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accuracy for analysis purposes. What matters during optimization is that the ;gy_Q f2U
changes the optimizer makes to the model affect the merit function in the same way kf_s.Dedw
that the overall performance is affected. It is possible to define the merit function so \% ! ]qv
that it has less accuracy and/or coarser mesh resolution than meshes used for X< K[`
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analysis and yet produce improvements during optimization, especially in the early kI]i ,v#F
stages of a design. 0aSN 8
A rule of thumb for the first Monte Carlo run on a system is to have an average of at IE: x&q`3
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays *58<.L|
on the receiver to achieve uniform distribution. It is likely that you will need to h eZJ(mR
define more rays than 800 in a simulation in order to get 800 rays on the receiver. oiJ a1X
When using simplified meshes as merit functions, you should check the before and
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after performance of a design to verify that the changes correlate to the changes of N'Ywn}!js
the merit function during optimization. As a design reaches its final performance C"k8 M\RW?
level, you will have to add rays to the simulation to reduce the noise floor so that Dd< gYPC
sufficient accuracy and mesh resolution are available for the optimizer to find the <tuh%k
best solution.
