How Many Rays Do I Need for Monte Carlo Optimization? g S xK9P
While it is important to ensure that a sufficient number of rays are traced to itCl CEOA
distinguish the merit function value from the noise floor, it is often not necessary to q_S`@2Dzz,
trace as many rays during optimization as you might to obtain a given level of 5mxHOtvtWM
accuracy for analysis purposes. What matters during optimization is that the {C3AxK0
changes the optimizer makes to the model affect the merit function in the same way mC OJ1 }
that the overall performance is affected. It is possible to define the merit function so =%P'?(o|
that it has less accuracy and/or coarser mesh resolution than meshes used for |`d,r.+P7
analysis and yet produce improvements during optimization, especially in the early {uH
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stages of a design. =.NZ {G
A rule of thumb for the first Monte Carlo run on a system is to have an average of at 8fA9y Q 8
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays KzD5>Xf]4$
on the receiver to achieve uniform distribution. It is likely that you will need to jm9J-%?
define more rays than 800 in a simulation in order to get 800 rays on the receiver. wgfy; #
When using simplified meshes as merit functions, you should check the before and > `L)E,=/
after performance of a design to verify that the changes correlate to the changes of lCT N
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the merit function during optimization. As a design reaches its final performance &* GwA
level, you will have to add rays to the simulation to reduce the noise floor so that ]+A>*0#"
sufficient accuracy and mesh resolution are available for the optimizer to find the J A0$Fz
best solution.
