How Many Rays Do I Need for Monte Carlo Optimization? `{ F8#
While it is important to ensure that a sufficient number of rays are traced to ^fnRzX
distinguish the merit function value from the noise floor, it is often not necessary to M%&`&{
trace as many rays during optimization as you might to obtain a given level of "793R^Tz
accuracy for analysis purposes. What matters during optimization is that the O|7q,bEm^
changes the optimizer makes to the model affect the merit function in the same way xZ`t~4qR
that the overall performance is affected. It is possible to define the merit function so 'r1 &zw(
that it has less accuracy and/or coarser mesh resolution than meshes used for Vl^jTX5N
analysis and yet produce improvements during optimization, especially in the early 8Mws?] \/q
stages of a design. %PlPXoG=
A rule of thumb for the first Monte Carlo run on a system is to have an average of at ?3KI}'}EM
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays P|HY=RM a
on the receiver to achieve uniform distribution. It is likely that you will need to (jY.S|%
define more rays than 800 in a simulation in order to get 800 rays on the receiver. J _rCo4}
When using simplified meshes as merit functions, you should check the before and 22tY%Y9
after performance of a design to verify that the changes correlate to the changes of ;1{S"UY
the merit function during optimization. As a design reaches its final performance IA8kq =W
level, you will have to add rays to the simulation to reduce the noise floor so that ODv)-J
sufficient accuracy and mesh resolution are available for the optimizer to find the k qwS/s
best solution.
