How Many Rays Do I Need for Monte Carlo Optimization? ZiR },F /
While it is important to ensure that a sufficient number of rays are traced to etnq{tE5
distinguish the merit function value from the noise floor, it is often not necessary to RbOEXH*]
trace as many rays during optimization as you might to obtain a given level of h"C7l#u
accuracy for analysis purposes. What matters during optimization is that the Ih Yso7g
changes the optimizer makes to the model affect the merit function in the same way !4;A"B(
that the overall performance is affected. It is possible to define the merit function so 0%x"Va~"z
that it has less accuracy and/or coarser mesh resolution than meshes used for U`)\|\NY
analysis and yet produce improvements during optimization, especially in the early qDSZ:36
stages of a design. ,<Ag&*YE4
A rule of thumb for the first Monte Carlo run on a system is to have an average of at qKt*<KGeY
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays L@m NfLK
on the receiver to achieve uniform distribution. It is likely that you will need to oe ( })M
define more rays than 800 in a simulation in order to get 800 rays on the receiver. +\Hh|Uz5
When using simplified meshes as merit functions, you should check the before and 7hV9nuW
after performance of a design to verify that the changes correlate to the changes of K)ZW1d;
the merit function during optimization. As a design reaches its final performance m-x nbTcQ
level, you will have to add rays to the simulation to reduce the noise floor so that RSv?imi=
sufficient accuracy and mesh resolution are available for the optimizer to find the sxG 8 jD
best solution.
