How Many Rays Do I Need for Monte Carlo Optimization? ;Q%19f3,6
While it is important to ensure that a sufficient number of rays are traced to m+JGe5fR<
distinguish the merit function value from the noise floor, it is often not necessary to 1 bx^Pt)
trace as many rays during optimization as you might to obtain a given level of 3 jQ$72_
accuracy for analysis purposes. What matters during optimization is that the gj(l&F *@
changes the optimizer makes to the model affect the merit function in the same way Ay. q )
that the overall performance is affected. It is possible to define the merit function so OjL"0imN6
that it has less accuracy and/or coarser mesh resolution than meshes used for jZgnt{
analysis and yet produce improvements during optimization, especially in the early $2T ty 7
stages of a design. do UqUak
A rule of thumb for the first Monte Carlo run on a system is to have an average of at !EC\1rmdlN
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays REe%>|
on the receiver to achieve uniform distribution. It is likely that you will need to 5L'@WB|{4u
define more rays than 800 in a simulation in order to get 800 rays on the receiver. X([n> w
When using simplified meshes as merit functions, you should check the before and ?>Ci`XlLr
after performance of a design to verify that the changes correlate to the changes of U8@*I>vA
the merit function during optimization. As a design reaches its final performance SOY#, Zu
level, you will have to add rays to the simulation to reduce the noise floor so that {d5ur@G1
sufficient accuracy and mesh resolution are available for the optimizer to find the ~<Qxw>S#
best solution.
