How Many Rays Do I Need for Monte Carlo Optimization? [eFJ+|U9
While it is important to ensure that a sufficient number of rays are traced to Ygc|9}
distinguish the merit function value from the noise floor, it is often not necessary to O8~RfB
trace as many rays during optimization as you might to obtain a given level of -$$mr U
accuracy for analysis purposes. What matters during optimization is that the tX6_n%/L
changes the optimizer makes to the model affect the merit function in the same way [5]n,toAh
that the overall performance is affected. It is possible to define the merit function so x[xRqC
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that it has less accuracy and/or coarser mesh resolution than meshes used for a(LtiO
analysis and yet produce improvements during optimization, especially in the early 8erG](
stages of a design. 3taGb>15
A rule of thumb for the first Monte Carlo run on a system is to have an average of at i,t!17M :
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays ^SK!? M
on the receiver to achieve uniform distribution. It is likely that you will need to jVh:Bw
define more rays than 800 in a simulation in order to get 800 rays on the receiver. z[@i=avPG
When using simplified meshes as merit functions, you should check the before and F\^\,hy
after performance of a design to verify that the changes correlate to the changes of L 1f=90
the merit function during optimization. As a design reaches its final performance BkP4.XRI
level, you will have to add rays to the simulation to reduce the noise floor so that 7$x%A&]
sufficient accuracy and mesh resolution are available for the optimizer to find the (\o4 c0UzK
best solution.
