How Many Rays Do I Need for Monte Carlo Optimization?
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While it is important to ensure that a sufficient number of rays are traced to -y+>^45
distinguish the merit function value from the noise floor, it is often not necessary to `)n4I:)2
trace as many rays during optimization as you might to obtain a given level of ?W' p&(;
accuracy for analysis purposes. What matters during optimization is that the w!7\wI [
changes the optimizer makes to the model affect the merit function in the same way [!A[oK9i C
that the overall performance is affected. It is possible to define the merit function so zzQWHg ]/
that it has less accuracy and/or coarser mesh resolution than meshes used for '3( ^Zv
analysis and yet produce improvements during optimization, especially in the early ,JV 0ib,
stages of a design. 9RaO[j`
A rule of thumb for the first Monte Carlo run on a system is to have an average of at XWp8[Cx s
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays y~
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on the receiver to achieve uniform distribution. It is likely that you will need to J/?Nf2L4
define more rays than 800 in a simulation in order to get 800 rays on the receiver. ~ y!'\d>q<
When using simplified meshes as merit functions, you should check the before and $>XeC}"x68
after performance of a design to verify that the changes correlate to the changes of i/ ilG 3m>
the merit function during optimization. As a design reaches its final performance W D[eoi
level, you will have to add rays to the simulation to reduce the noise floor so that c[d zO .~
sufficient accuracy and mesh resolution are available for the optimizer to find the /9w>:i81
best solution.
