How Many Rays Do I Need for Monte Carlo Optimization? qi !Nv$e
While it is important to ensure that a sufficient number of rays are traced to L8"0o 0-
distinguish the merit function value from the noise floor, it is often not necessary to HFV4S]U=
trace as many rays during optimization as you might to obtain a given level of E3bS Q
accuracy for analysis purposes. What matters during optimization is that the N; 4tvW I
changes the optimizer makes to the model affect the merit function in the same way pa 1.+ ~)
that the overall performance is affected. It is possible to define the merit function so e.X*x4*>~
that it has less accuracy and/or coarser mesh resolution than meshes used for OV)J
analysis and yet produce improvements during optimization, especially in the early hrsMAh!
stages of a design. >0yx!Iao
A rule of thumb for the first Monte Carlo run on a system is to have an average of at > ^vyp!
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays e))fbv&V
on the receiver to achieve uniform distribution. It is likely that you will need to -8;@NAUa
define more rays than 800 in a simulation in order to get 800 rays on the receiver. 4L'dV
When using simplified meshes as merit functions, you should check the before and DQ'yFPE
after performance of a design to verify that the changes correlate to the changes of 2 , bo
the merit function during optimization. As a design reaches its final performance *`]LbS
level, you will have to add rays to the simulation to reduce the noise floor so that R0>GM`{
sufficient accuracy and mesh resolution are available for the optimizer to find the ? OrRTRW
best solution.
