How Many Rays Do I Need for Monte Carlo Optimization? Qvt
While it is important to ensure that a sufficient number of rays are traced to 9q;n@q:29
distinguish the merit function value from the noise floor, it is often not necessary to o_hk!s^4m
trace as many rays during optimization as you might to obtain a given level of -@ f5d
accuracy for analysis purposes. What matters during optimization is that the d[ (KgX9
changes the optimizer makes to the model affect the merit function in the same way 9`eu&n@Z
that the overall performance is affected. It is possible to define the merit function so v1wMXOR
that it has less accuracy and/or coarser mesh resolution than meshes used for 57*`y'C W
analysis and yet produce improvements during optimization, especially in the early 9B gR@b
stages of a design. Lq.aM.& ;#
A rule of thumb for the first Monte Carlo run on a system is to have an average of at %7WGodlXW
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays M:i;;)cq
on the receiver to achieve uniform distribution. It is likely that you will need to $6Z@0H@X
define more rays than 800 in a simulation in order to get 800 rays on the receiver. }_m/3*x_
When using simplified meshes as merit functions, you should check the before and ^y"5pf SR
after performance of a design to verify that the changes correlate to the changes of $/sIdFZi
the merit function during optimization. As a design reaches its final performance jA9&hbQuL
level, you will have to add rays to the simulation to reduce the noise floor so that iX,| ;J|]
sufficient accuracy and mesh resolution are available for the optimizer to find the dV(61C0wn
best solution.
