How Many Rays Do I Need for Monte Carlo Optimization? rWvJ{ -%
While it is important to ensure that a sufficient number of rays are traced to L7xTAFe
distinguish the merit function value from the noise floor, it is often not necessary to 3]VTQl{P
trace as many rays during optimization as you might to obtain a given level of `aI%laj&M
accuracy for analysis purposes. What matters during optimization is that the rz.`$b
changes the optimizer makes to the model affect the merit function in the same way )O&$-4gL'
that the overall performance is affected. It is possible to define the merit function so aVtwpkgZ
that it has less accuracy and/or coarser mesh resolution than meshes used for ZK'I$p]b
analysis and yet produce improvements during optimization, especially in the early oL6_Ya
stages of a design. gVl#pVO`N
A rule of thumb for the first Monte Carlo run on a system is to have an average of at A qm0|GlJ
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays c& _ 3"2:
on the receiver to achieve uniform distribution. It is likely that you will need to oD}I{&=wa
define more rays than 800 in a simulation in order to get 800 rays on the receiver. T@S +5(
When using simplified meshes as merit functions, you should check the before and W@0(Y9jdg
after performance of a design to verify that the changes correlate to the changes of ]JM9 ^F
the merit function during optimization. As a design reaches its final performance _88~uYG
level, you will have to add rays to the simulation to reduce the noise floor so that ! 9N%=6\
sufficient accuracy and mesh resolution are available for the optimizer to find the p{U 8z\
best solution.
