How Many Rays Do I Need for Monte Carlo Optimization? a| 7a_s4(
While it is important to ensure that a sufficient number of rays are traced to y
!$alE
distinguish the merit function value from the noise floor, it is often not necessary to 8I;XS14Q
trace as many rays during optimization as you might to obtain a given level of pCh2SQ(Q>
accuracy for analysis purposes. What matters during optimization is that the =3ioQZ^Vz
changes the optimizer makes to the model affect the merit function in the same way !~ ]<$WZV
that the overall performance is affected. It is possible to define the merit function so Sq\(pfv o
that it has less accuracy and/or coarser mesh resolution than meshes used for l:#-d.z#
analysis and yet produce improvements during optimization, especially in the early Vwk #qgnX
stages of a design. r}#\BbCv;7
A rule of thumb for the first Monte Carlo run on a system is to have an average of at 0& >H^
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays 0+Ta%H{
on the receiver to achieve uniform distribution. It is likely that you will need to %p^.|Me7
define more rays than 800 in a simulation in order to get 800 rays on the receiver. dovZ#D@Q
When using simplified meshes as merit functions, you should check the before and x<Vm5j
after performance of a design to verify that the changes correlate to the changes of ;5-r_D;9
the merit function during optimization. As a design reaches its final performance )SryD RT
level, you will have to add rays to the simulation to reduce the noise floor so that
RlT3Iz;
sufficient accuracy and mesh resolution are available for the optimizer to find the 0:0NXVYs&
best solution.
