How Many Rays Do I Need for Monte Carlo Optimization? TVk C pO,H
While it is important to ensure that a sufficient number of rays are traced to TA2?Ia;@xV
distinguish the merit function value from the noise floor, it is often not necessary to o=&tT,z
trace as many rays during optimization as you might to obtain a given level of ]vFtByqn
accuracy for analysis purposes. What matters during optimization is that the TJ&Z/k3-
changes the optimizer makes to the model affect the merit function in the same way 5IwQ <V
that the overall performance is affected. It is possible to define the merit function so U{8]TEv
that it has less accuracy and/or coarser mesh resolution than meshes used for MmZs|pXk
analysis and yet produce improvements during optimization, especially in the early O&]P
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stages of a design. }i)^?@
A rule of thumb for the first Monte Carlo run on a system is to have an average of at qu}&4_`%:V
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays U_X /
on the receiver to achieve uniform distribution. It is likely that you will need to l8$7N=Y
define more rays than 800 in a simulation in order to get 800 rays on the receiver. #>]o' KQx
When using simplified meshes as merit functions, you should check the before and (jV_L 1D
after performance of a design to verify that the changes correlate to the changes of "_g3{[es!
the merit function during optimization. As a design reaches its final performance Za*QX|
level, you will have to add rays to the simulation to reduce the noise floor so that QR.] ?t;1
sufficient accuracy and mesh resolution are available for the optimizer to find the vE}>PEfA
best solution.
