How Many Rays Do I Need for Monte Carlo Optimization? :bL^S1et
While it is important to ensure that a sufficient number of rays are traced to /b/ 6*&
distinguish the merit function value from the noise floor, it is often not necessary to ~PU}==*q
trace as many rays during optimization as you might to obtain a given level of .Yz^r?3t
accuracy for analysis purposes. What matters during optimization is that the !f}D*8\f
changes the optimizer makes to the model affect the merit function in the same way ~-uDN)
that the overall performance is affected. It is possible to define the merit function so P{Q$(rOe
that it has less accuracy and/or coarser mesh resolution than meshes used for %:Y(x$Qy
analysis and yet produce improvements during optimization, especially in the early 0%,?z`UY
stages of a design. Hw62'%
A rule of thumb for the first Monte Carlo run on a system is to have an average of at 2MW7nIEs
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays {XD':2E
on the receiver to achieve uniform distribution. It is likely that you will need to _'^_9u G
define more rays than 800 in a simulation in order to get 800 rays on the receiver. +8"P*z,
When using simplified meshes as merit functions, you should check the before and uD[T l
after performance of a design to verify that the changes correlate to the changes of <AP.m4N) _
the merit function during optimization. As a design reaches its final performance 2^nws
level, you will have to add rays to the simulation to reduce the noise floor so that KuL+~
sufficient accuracy and mesh resolution are available for the optimizer to find the L>0Pur) [
best solution.