How Many Rays Do I Need for Monte Carlo Optimization? a6 Vfd&
While it is important to ensure that a sufficient number of rays are traced to ,^9+G"H:I
distinguish the merit function value from the noise floor, it is often not necessary to *7A B0y0k
trace as many rays during optimization as you might to obtain a given level of 64'2ICf#m
accuracy for analysis purposes. What matters during optimization is that the ^G! cv
changes the optimizer makes to the model affect the merit function in the same way /\1'.GR
that the overall performance is affected. It is possible to define the merit function so _7!ZnJrR
that it has less accuracy and/or coarser mesh resolution than meshes used for t|U5]$5
analysis and yet produce improvements during optimization, especially in the early 6u_ i >z
stages of a design. \&F4Wl>`
A rule of thumb for the first Monte Carlo run on a system is to have an average of at <X*o W ".
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays _hY6 NMw
on the receiver to achieve uniform distribution. It is likely that you will need to sc*R:"
define more rays than 800 in a simulation in order to get 800 rays on the receiver. %bw+>:Tr
When using simplified meshes as merit functions, you should check the before and Zh8\B)0unn
after performance of a design to verify that the changes correlate to the changes of [8'?G5/n
the merit function during optimization. As a design reaches its final performance N8 2 6xvA
level, you will have to add rays to the simulation to reduce the noise floor so that ,7V?K j
sufficient accuracy and mesh resolution are available for the optimizer to find the {IOc'W-C#2
best solution.
