How Many Rays Do I Need for Monte Carlo Optimization? \0h/~3
While it is important to ensure that a sufficient number of rays are traced to Bv8C_-lV/
distinguish the merit function value from the noise floor, it is often not necessary to zwtsw [.
trace as many rays during optimization as you might to obtain a given level of vXbT E$
accuracy for analysis purposes. What matters during optimization is that the `Tj}4f
changes the optimizer makes to the model affect the merit function in the same way 4:$>, D\
that the overall performance is affected. It is possible to define the merit function so {O) &5
that it has less accuracy and/or coarser mesh resolution than meshes used for M-N2>i #
analysis and yet produce improvements during optimization, especially in the early !Yu-a!
stages of a design. 1 1CJT
A rule of thumb for the first Monte Carlo run on a system is to have an average of at 5H+k_U
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays P~e$iBH'
on the receiver to achieve uniform distribution. It is likely that you will need to )_cv}. xe
define more rays than 800 in a simulation in order to get 800 rays on the receiver. "ux]kfoT
When using simplified meshes as merit functions, you should check the before and \BXVWE|
after performance of a design to verify that the changes correlate to the changes of p8l#=]\ ;
the merit function during optimization. As a design reaches its final performance 8n5nHne
level, you will have to add rays to the simulation to reduce the noise floor so that 7-I>5 3@
sufficient accuracy and mesh resolution are available for the optimizer to find the I })t
best solution.
