How Many Rays Do I Need for Monte Carlo Optimization? 5V/&4$.U!
While it is important to ensure that a sufficient number of rays are traced to gS<p~LPf
distinguish the merit function value from the noise floor, it is often not necessary to _m?i$5
trace as many rays during optimization as you might to obtain a given level of :;Z/$M16B
accuracy for analysis purposes. What matters during optimization is that the esTL3 l{[
changes the optimizer makes to the model affect the merit function in the same way Ne+Rs+~4
that the overall performance is affected. It is possible to define the merit function so d [l8qaD
that it has less accuracy and/or coarser mesh resolution than meshes used for [!%5( Ro_
analysis and yet produce improvements during optimization, especially in the early /E<Q_/'Z
stages of a design. ThX3@o
A rule of thumb for the first Monte Carlo run on a system is to have an average of at xBxiBhqzF
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays E|;>!MMA;
on the receiver to achieve uniform distribution. It is likely that you will need to Jf2JGTcm
define more rays than 800 in a simulation in order to get 800 rays on the receiver. X[?f U&
When using simplified meshes as merit functions, you should check the before and poafGoH-Y
after performance of a design to verify that the changes correlate to the changes of #9(+)~irz`
the merit function during optimization. As a design reaches its final performance ]mtiIu [
level, you will have to add rays to the simulation to reduce the noise floor so that W^3 Jg2gE
sufficient accuracy and mesh resolution are available for the optimizer to find the W]Xwt'ABz
best solution.
