How Many Rays Do I Need for Monte Carlo Optimization? ibha`
While it is important to ensure that a sufficient number of rays are traced to 8!sl) R
distinguish the merit function value from the noise floor, it is often not necessary to :A"GO c,
trace as many rays during optimization as you might to obtain a given level of 'Y `or14E
accuracy for analysis purposes. What matters during optimization is that the /d*d '3{c
changes the optimizer makes to the model affect the merit function in the same way ,Tjc\;~%
that the overall performance is affected. It is possible to define the merit function so FbhF45H
that it has less accuracy and/or coarser mesh resolution than meshes used for |U)M.\h
analysis and yet produce improvements during optimization, especially in the early t[VA|1gG
stages of a design. =)!sWY:
A rule of thumb for the first Monte Carlo run on a system is to have an average of at 4 J{6Wt";
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays *d b,N'rK
on the receiver to achieve uniform distribution. It is likely that you will need to G*^4+^Vz?
define more rays than 800 in a simulation in order to get 800 rays on the receiver. >8PGyc*9
When using simplified meshes as merit functions, you should check the before and V^apDV\AV
after performance of a design to verify that the changes correlate to the changes of N SI$uS6
the merit function during optimization. As a design reaches its final performance _TEjB:9eY
level, you will have to add rays to the simulation to reduce the noise floor so that 9 Zw{MM ]
sufficient accuracy and mesh resolution are available for the optimizer to find the 4d-f 6iiFV
best solution.
