How Many Rays Do I Need for Monte Carlo Optimization? <*E{z r&
While it is important to ensure that a sufficient number of rays are traced to P dcF
distinguish the merit function value from the noise floor, it is often not necessary to Adp:O"-H1o
trace as many rays during optimization as you might to obtain a given level of 2siUpmX
accuracy for analysis purposes. What matters during optimization is that the D_ybgX?0:
changes the optimizer makes to the model affect the merit function in the same way ^o}!=aMr
that the overall performance is affected. It is possible to define the merit function so jFf2( AR
that it has less accuracy and/or coarser mesh resolution than meshes used for Y [k%<f
analysis and yet produce improvements during optimization, especially in the early NTS
tk{s,
stages of a design. u1s^AW8 y
A rule of thumb for the first Monte Carlo run on a system is to have an average of at ) E.KB6
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays rC
V&& 09
on the receiver to achieve uniform distribution. It is likely that you will need to p8o
~
define more rays than 800 in a simulation in order to get 800 rays on the receiver. Wly-z$\
When using simplified meshes as merit functions, you should check the before and XP~bmh,T,
after performance of a design to verify that the changes correlate to the changes of 6 "U&i9
the merit function during optimization. As a design reaches its final performance TkXD#%nFY
level, you will have to add rays to the simulation to reduce the noise floor so that L\|p8jJ
sufficient accuracy and mesh resolution are available for the optimizer to find the <yz)iCU?
best solution.
