How Many Rays Do I Need for Monte Carlo Optimization? RrZjC
While it is important to ensure that a sufficient number of rays are traced to -KNJCcBJ
distinguish the merit function value from the noise floor, it is often not necessary to 'j 3'n0o
trace as many rays during optimization as you might to obtain a given level of ;Z`)*TRp4
accuracy for analysis purposes. What matters during optimization is that the |TUpv*pq
changes the optimizer makes to the model affect the merit function in the same way {PVu3 W
that the overall performance is affected. It is possible to define the merit function so :> q?s
that it has less accuracy and/or coarser mesh resolution than meshes used for cY"^3Ot%^
analysis and yet produce improvements during optimization, especially in the early |"-,C} O
stages of a design. *(scSC>
A rule of thumb for the first Monte Carlo run on a system is to have an average of at ]s -6GT
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays `P5"5N\h
on the receiver to achieve uniform distribution. It is likely that you will need to "|G,P-5G "
define more rays than 800 in a simulation in order to get 800 rays on the receiver. 5->PDp
When using simplified meshes as merit functions, you should check the before and ;?o C=c
after performance of a design to verify that the changes correlate to the changes of f!J^vDl
the merit function during optimization. As a design reaches its final performance $F-XXBp
level, you will have to add rays to the simulation to reduce the noise floor so that \S<5b&G
sufficient accuracy and mesh resolution are available for the optimizer to find the ,pASjFWi
best solution.
