How Many Rays Do I Need for Monte Carlo Optimization? ~E~J*R Ze
While it is important to ensure that a sufficient number of rays are traced to FW,D\51pTP
distinguish the merit function value from the noise floor, it is often not necessary to ?;Qk!t2U
trace as many rays during optimization as you might to obtain a given level of v2Y=vr
accuracy for analysis purposes. What matters during optimization is that the WBIS
changes the optimizer makes to the model affect the merit function in the same way hFv}JQJw<
that the overall performance is affected. It is possible to define the merit function so m~A/.t%=
that it has less accuracy and/or coarser mesh resolution than meshes used for |Rc#Q<Vh|
analysis and yet produce improvements during optimization, especially in the early )2S\ :&x
stages of a design. "AVc^ >
A rule of thumb for the first Monte Carlo run on a system is to have an average of at 2 )YLs5>W%
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays ai RNd~\
on the receiver to achieve uniform distribution. It is likely that you will need to Pe. D[]S
define more rays than 800 in a simulation in order to get 800 rays on the receiver. 511q\w M
When using simplified meshes as merit functions, you should check the before and `1gsrHi4N
after performance of a design to verify that the changes correlate to the changes of mUxD.;P
the merit function during optimization. As a design reaches its final performance y- mmc}B>N
level, you will have to add rays to the simulation to reduce the noise floor so that OJaU,vQ#
sufficient accuracy and mesh resolution are available for the optimizer to find the Z " u/8
best solution.
