How Many Rays Do I Need for Monte Carlo Optimization? @GiR~bKZ
While it is important to ensure that a sufficient number of rays are traced to %aszZP
distinguish the merit function value from the noise floor, it is often not necessary to _Vq7Gxy$R
trace as many rays during optimization as you might to obtain a given level of j> 0~"A
accuracy for analysis purposes. What matters during optimization is that the 7o4 vf~
changes the optimizer makes to the model affect the merit function in the same way K{&b "Ba1
that the overall performance is affected. It is possible to define the merit function so =!`\ =!y
that it has less accuracy and/or coarser mesh resolution than meshes used for i Y2%_b!5
analysis and yet produce improvements during optimization, especially in the early &Tf R].
stages of a design. d"> Ya !W
A rule of thumb for the first Monte Carlo run on a system is to have an average of at \ O7?!i
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays E+&]96*Lby
on the receiver to achieve uniform distribution. It is likely that you will need to ^ 8V cm*
define more rays than 800 in a simulation in order to get 800 rays on the receiver. (Rc 0 l;
When using simplified meshes as merit functions, you should check the before and CKTD27})
after performance of a design to verify that the changes correlate to the changes of H5N(MihT
the merit function during optimization. As a design reaches its final performance -e{H 8 ro
level, you will have to add rays to the simulation to reduce the noise floor so that -^ (NIl'
sufficient accuracy and mesh resolution are available for the optimizer to find the IrRn@15,
best solution.
