How Many Rays Do I Need for Monte Carlo Optimization? Kf:2%_DB
While it is important to ensure that a sufficient number of rays are traced to (ZE%tbm2
distinguish the merit function value from the noise floor, it is often not necessary to ')AByD}Hi]
trace as many rays during optimization as you might to obtain a given level of #6*V7@9]3|
accuracy for analysis purposes. What matters during optimization is that the Z-4K?;g'k
changes the optimizer makes to the model affect the merit function in the same way -vv
that the overall performance is affected. It is possible to define the merit function so BpQ;w,sefq
that it has less accuracy and/or coarser mesh resolution than meshes used for =,&u_>Dp
analysis and yet produce improvements during optimization, especially in the early $\0cJ CQ3
stages of a design. o
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A rule of thumb for the first Monte Carlo run on a system is to have an average of at VRtbHam
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays ppwd-^f3j
on the receiver to achieve uniform distribution. It is likely that you will need to |QnUK5D$
define more rays than 800 in a simulation in order to get 800 rays on the receiver. 17V\2=Io
When using simplified meshes as merit functions, you should check the before and t Y:G54d=_
after performance of a design to verify that the changes correlate to the changes of T4V[R N
the merit function during optimization. As a design reaches its final performance X)FL[RO%q
level, you will have to add rays to the simulation to reduce the noise floor so that k bfuvJ>
sufficient accuracy and mesh resolution are available for the optimizer to find the G*)s% 2c>h
best solution.
