How Many Rays Do I Need for Monte Carlo Optimization? (hpTJsZ
While it is important to ensure that a sufficient number of rays are traced to ,@}W@GGP)
distinguish the merit function value from the noise floor, it is often not necessary to *d^9,GGn-
trace as many rays during optimization as you might to obtain a given level of !8wZw68"
accuracy for analysis purposes. What matters during optimization is that the imo'(j7
changes the optimizer makes to the model affect the merit function in the same way X=fPGyhZ
that the overall performance is affected. It is possible to define the merit function so `DI{wqV9
that it has less accuracy and/or coarser mesh resolution than meshes used for hCU)W1q#
analysis and yet produce improvements during optimization, especially in the early gcX5Q^`a=
stages of a design. :X3rd|;kc
A rule of thumb for the first Monte Carlo run on a system is to have an average of at 4aj[5fhb-
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays 2 v"wWap-+
on the receiver to achieve uniform distribution. It is likely that you will need to r$b:1 C~
define more rays than 800 in a simulation in order to get 800 rays on the receiver. O4 lxeiRgC
When using simplified meshes as merit functions, you should check the before and F6RyOUma
after performance of a design to verify that the changes correlate to the changes of <'g0il
the merit function during optimization. As a design reaches its final performance o%vI kXw
level, you will have to add rays to the simulation to reduce the noise floor so that mJwv&E
sufficient accuracy and mesh resolution are available for the optimizer to find the 2A dX)iF@
best solution.
