How Many Rays Do I Need for Monte Carlo Optimization? ;%>X+/.y0
While it is important to ensure that a sufficient number of rays are traced to 4^6Oh#p0
distinguish the merit function value from the noise floor, it is often not necessary to "1P2`Ep;
trace as many rays during optimization as you might to obtain a given level of >X>]QMfh
accuracy for analysis purposes. What matters during optimization is that the &t@ $]m(
changes the optimizer makes to the model affect the merit function in the same way m|Z[8Tup
that the overall performance is affected. It is possible to define the merit function so oY@]&A^ah
that it has less accuracy and/or coarser mesh resolution than meshes used for Eh`W J~
analysis and yet produce improvements during optimization, especially in the early M
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stages of a design. )gR3S%Ju
A rule of thumb for the first Monte Carlo run on a system is to have an average of at ,5sv;
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays .L]2g$W\p
on the receiver to achieve uniform distribution. It is likely that you will need to iVqF]2>
define more rays than 800 in a simulation in order to get 800 rays on the receiver. Ki)hr%UFw
When using simplified meshes as merit functions, you should check the before and D{t0OvQag
after performance of a design to verify that the changes correlate to the changes of 2[Qzx%Vp
the merit function during optimization. As a design reaches its final performance z8};(I>)
level, you will have to add rays to the simulation to reduce the noise floor so that r^\Wo7q
sufficient accuracy and mesh resolution are available for the optimizer to find the lFgE{;z@
best solution.