How Many Rays Do I Need for Monte Carlo Optimization? b % L8mX
While it is important to ensure that a sufficient number of rays are traced to Dz:A.x@$*
distinguish the merit function value from the noise floor, it is often not necessary to qxf!]jm
trace as many rays during optimization as you might to obtain a given level of b4Z Zy w
accuracy for analysis purposes. What matters during optimization is that the UX;?~X
changes the optimizer makes to the model affect the merit function in the same way 7/a[;`i*!
that the overall performance is affected. It is possible to define the merit function so U748$%}]
that it has less accuracy and/or coarser mesh resolution than meshes used for F$ShhZgi
analysis and yet produce improvements during optimization, especially in the early P {i \x#
stages of a design. ci0)kxUB F
A rule of thumb for the first Monte Carlo run on a system is to have an average of at ri 1D*CS
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays g&!UaJ[#9
on the receiver to achieve uniform distribution. It is likely that you will need to 5,})x]'x
define more rays than 800 in a simulation in order to get 800 rays on the receiver. -;20|US) u
When using simplified meshes as merit functions, you should check the before and ^=.R#zrc
after performance of a design to verify that the changes correlate to the changes of L3GA]TI f
the merit function during optimization. As a design reaches its final performance BCYTlxC'
level, you will have to add rays to the simulation to reduce the noise floor so that x^Q:U 1
sufficient accuracy and mesh resolution are available for the optimizer to find the aY}:9qBice
best solution.
