How Many Rays Do I Need for Monte Carlo Optimization? d=\TC'd"{
While it is important to ensure that a sufficient number of rays are traced to Ii3F|Vb G
distinguish the merit function value from the noise floor, it is often not necessary to Y HgNL LZ?
trace as many rays during optimization as you might to obtain a given level of kTzO4s?
accuracy for analysis purposes. What matters during optimization is that the 4 F -<j!
changes the optimizer makes to the model affect the merit function in the same way r_8;aPL
that the overall performance is affected. It is possible to define the merit function so `Y!8,( 5#
that it has less accuracy and/or coarser mesh resolution than meshes used for =Y^K
analysis and yet produce improvements during optimization, especially in the early \,m*CYs`
stages of a design. L*rCUv `
A rule of thumb for the first Monte Carlo run on a system is to have an average of at Q"!GdKM
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays G ` eU
on the receiver to achieve uniform distribution. It is likely that you will need to 2#qc YU
define more rays than 800 in a simulation in order to get 800 rays on the receiver. 9 %Vy,
When using simplified meshes as merit functions, you should check the before and qm9=Ga5
after performance of a design to verify that the changes correlate to the changes of $E 8 }||d
the merit function during optimization. As a design reaches its final performance 'aeuL1mz
level, you will have to add rays to the simulation to reduce the noise floor so that F *U.cJ%
sufficient accuracy and mesh resolution are available for the optimizer to find the A58P$#)?
best solution.
