How Many Rays Do I Need for Monte Carlo Optimization? NO :a;
While it is important to ensure that a sufficient number of rays are traced to GgKEP,O
distinguish the merit function value from the noise floor, it is often not necessary to 2#k5+?-c61
trace as many rays during optimization as you might to obtain a given level of F :a ILx
accuracy for analysis purposes. What matters during optimization is that the *?M GMhE
changes the optimizer makes to the model affect the merit function in the same way NIw\}[-Z0E
that the overall performance is affected. It is possible to define the merit function so |fo0
that it has less accuracy and/or coarser mesh resolution than meshes used for :,)lm.}]t
analysis and yet produce improvements during optimization, especially in the early ({o'd=nO
stages of a design. p) +k=b
A rule of thumb for the first Monte Carlo run on a system is to have an average of at / &4U6a
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays 0]4(:(B
on the receiver to achieve uniform distribution. It is likely that you will need to 0V ?F'<qy
define more rays than 800 in a simulation in order to get 800 rays on the receiver. vx4+QQY P
When using simplified meshes as merit functions, you should check the before and K<>sOWZ'S
after performance of a design to verify that the changes correlate to the changes of &4_qF^9J
the merit function during optimization. As a design reaches its final performance \QB;Ja _
level, you will have to add rays to the simulation to reduce the noise floor so that 0 iJue &
sufficient accuracy and mesh resolution are available for the optimizer to find the 33}oO,}t,
best solution.
