How Many Rays Do I Need for Monte Carlo Optimization? t.Hte/, k
While it is important to ensure that a sufficient number of rays are traced to #M$Gj>E%4
distinguish the merit function value from the noise floor, it is often not necessary to kd^ C Z;O
trace as many rays during optimization as you might to obtain a given level of k fS44NV
accuracy for analysis purposes. What matters during optimization is that the
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changes the optimizer makes to the model affect the merit function in the same way O^(ji8[l
that the overall performance is affected. It is possible to define the merit function so )`5k fj
that it has less accuracy and/or coarser mesh resolution than meshes used for $o KT-G
analysis and yet produce improvements during optimization, especially in the early tVJ}NI #
stages of a design. ?g*#l d ()
A rule of thumb for the first Monte Carlo run on a system is to have an average of at f4A evh:
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays 1"k"<{%
on the receiver to achieve uniform distribution. It is likely that you will need to It.G-(
define more rays than 800 in a simulation in order to get 800 rays on the receiver. \]pRu"
When using simplified meshes as merit functions, you should check the before and =@w,D.5h
after performance of a design to verify that the changes correlate to the changes of }S84^2J_
the merit function during optimization. As a design reaches its final performance aq/'2U 7
level, you will have to add rays to the simulation to reduce the noise floor so that W8hf
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sufficient accuracy and mesh resolution are available for the optimizer to find the .{U@Hva_K
best solution.
