How Many Rays Do I Need for Monte Carlo Optimization? G=KXA'R)1.
While it is important to ensure that a sufficient number of rays are traced to >ZnnGX6$(
distinguish the merit function value from the noise floor, it is often not necessary to Cuom_+wV&
trace as many rays during optimization as you might to obtain a given level of }Q;^C
accuracy for analysis purposes. What matters during optimization is that the 6dqI{T-i ?
changes the optimizer makes to the model affect the merit function in the same way OT 3~5j1[
that the overall performance is affected. It is possible to define the merit function so [T2!,D.
that it has less accuracy and/or coarser mesh resolution than meshes used for AK$i0Rn;pm
analysis and yet produce improvements during optimization, especially in the early +!-U+W
stages of a design. (U|WP%IM'
A rule of thumb for the first Monte Carlo run on a system is to have an average of at AZbFj-^4
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays G;^,T/q47
on the receiver to achieve uniform distribution. It is likely that you will need to xL!@$;J
define more rays than 800 in a simulation in order to get 800 rays on the receiver. @F!oRm5
When using simplified meshes as merit functions, you should check the before and *#o2b-[V
after performance of a design to verify that the changes correlate to the changes of w+{ o^ O
the merit function during optimization. As a design reaches its final performance /}3I:aJwb
level, you will have to add rays to the simulation to reduce the noise floor so that G~zP&9N|
sufficient accuracy and mesh resolution are available for the optimizer to find the na]
9-~4
best solution.
