How Many Rays Do I Need for Monte Carlo Optimization? %}T' 3
While it is important to ensure that a sufficient number of rays are traced to QqpXUyHp[
distinguish the merit function value from the noise floor, it is often not necessary to I_QWdxn
trace as many rays during optimization as you might to obtain a given level of nT(Lh/
accuracy for analysis purposes. What matters during optimization is that the *@2+$fg z
changes the optimizer makes to the model affect the merit function in the same way BZ2frG\0&I
that the overall performance is affected. It is possible to define the merit function so ^oykimYI-
that it has less accuracy and/or coarser mesh resolution than meshes used for w(>mP9Cb
analysis and yet produce improvements during optimization, especially in the early ~ "eQPTd
stages of a design. A6a r@$MZ
A rule of thumb for the first Monte Carlo run on a system is to have an average of at I.C,y\
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays ]@Gw$
on the receiver to achieve uniform distribution. It is likely that you will need to ;nzzt~aCC
define more rays than 800 in a simulation in order to get 800 rays on the receiver. UbWeE,T~S
When using simplified meshes as merit functions, you should check the before and hn$l<8=Q_
after performance of a design to verify that the changes correlate to the changes of e}F 1ZJz
the merit function during optimization. As a design reaches its final performance ,CGq_>Z
level, you will have to add rays to the simulation to reduce the noise floor so that VLLE0W _]
sufficient accuracy and mesh resolution are available for the optimizer to find the OI@;ffHSW
best solution.
