How Many Rays Do I Need for Monte Carlo Optimization? /|p6NK;8L
While it is important to ensure that a sufficient number of rays are traced to >~* }9y0$
distinguish the merit function value from the noise floor, it is often not necessary to S7( tGD
trace as many rays during optimization as you might to obtain a given level of :&J1#% t
accuracy for analysis purposes. What matters during optimization is that the pM}n)Q!{3"
changes the optimizer makes to the model affect the merit function in the same way HQGH7<=Om
that the overall performance is affected. It is possible to define the merit function so |* B9{/;4
that it has less accuracy and/or coarser mesh resolution than meshes used for ImsyyeY]
analysis and yet produce improvements during optimization, especially in the early ?fX`z(Z
stages of a design. `%_ (_%K
A rule of thumb for the first Monte Carlo run on a system is to have an average of at _18Aek
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays md;jj^8zj
on the receiver to achieve uniform distribution. It is likely that you will need to (05a 9
define more rays than 800 in a simulation in order to get 800 rays on the receiver. p9[gG\
When using simplified meshes as merit functions, you should check the before and n'8 3P%x
after performance of a design to verify that the changes correlate to the changes of K'oy6$B
the merit function during optimization. As a design reaches its final performance 7Cx-yv
level, you will have to add rays to the simulation to reduce the noise floor so that zxC~a97`
sufficient accuracy and mesh resolution are available for the optimizer to find the wUKt$_]``
best solution.
