How Many Rays Do I Need for Monte Carlo Optimization? oq vu8"
While it is important to ensure that a sufficient number of rays are traced to nN& dtjoF
distinguish the merit function value from the noise floor, it is often not necessary to $@6q5Iz!&
trace as many rays during optimization as you might to obtain a given level of Vl$RMW@Ds
accuracy for analysis purposes. What matters during optimization is that the 3DO* kM1s@
changes the optimizer makes to the model affect the merit function in the same way q2xAx1R`sV
that the overall performance is affected. It is possible to define the merit function so j?C[ids<
that it has less accuracy and/or coarser mesh resolution than meshes used for (tA[] ne 2
analysis and yet produce improvements during optimization, especially in the early EJ
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stages of a design. C)m@/w
A rule of thumb for the first Monte Carlo run on a system is to have an average of at 06HU6d ,
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays z2V ->UK)
on the receiver to achieve uniform distribution. It is likely that you will need to @8 c@H#H
define more rays than 800 in a simulation in order to get 800 rays on the receiver. +ase>'<N#
When using simplified meshes as merit functions, you should check the before and z>+CMH5L)
after performance of a design to verify that the changes correlate to the changes of ]iTP5~8U
the merit function during optimization. As a design reaches its final performance hD# Mhy5h
level, you will have to add rays to the simulation to reduce the noise floor so that c*#$sZ@YA
sufficient accuracy and mesh resolution are available for the optimizer to find the i+S%e ,U*
best solution.
