How Many Rays Do I Need for Monte Carlo Optimization? i3>_E <"9
While it is important to ensure that a sufficient number of rays are traced to <co:z<^lqu
distinguish the merit function value from the noise floor, it is often not necessary to W^eQ}A+Z
trace as many rays during optimization as you might to obtain a given level of LDDt=HEY4
accuracy for analysis purposes. What matters during optimization is that the _ ?XR;2 ]
changes the optimizer makes to the model affect the merit function in the same way "a<:fEsSE
that the overall performance is affected. It is possible to define the merit function so .AF\[IQ
that it has less accuracy and/or coarser mesh resolution than meshes used for OSwum!hzN
analysis and yet produce improvements during optimization, especially in the early unr`.}A2>
stages of a design. %)e&"mq!|
A rule of thumb for the first Monte Carlo run on a system is to have an average of at w4RtIDW:
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays Z0M|B v9_
on the receiver to achieve uniform distribution. It is likely that you will need to .pb lI
define more rays than 800 in a simulation in order to get 800 rays on the receiver. p`'3Il3
When using simplified meshes as merit functions, you should check the before and "~"=e
after performance of a design to verify that the changes correlate to the changes of QjTs$#eMW
the merit function during optimization. As a design reaches its final performance 66po SZR@
level, you will have to add rays to the simulation to reduce the noise floor so that m-S e-aF
sufficient accuracy and mesh resolution are available for the optimizer to find the R l)g[s
best solution.
