How Many Rays Do I Need for Monte Carlo Optimization? qim
'dp:
While it is important to ensure that a sufficient number of rays are traced to k{V E1@
distinguish the merit function value from the noise floor, it is often not necessary to '{ [5M!B
trace as many rays during optimization as you might to obtain a given level of }U
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accuracy for analysis purposes. What matters during optimization is that the ^/5XZ} *
changes the optimizer makes to the model affect the merit function in the same way N`E-+9L)
that the overall performance is affected. It is possible to define the merit function so $ ''9K
that it has less accuracy and/or coarser mesh resolution than meshes used for !r`, =jK"
analysis and yet produce improvements during optimization, especially in the early ]uspx [UIc
stages of a design. gt YAH i
A rule of thumb for the first Monte Carlo run on a system is to have an average of at n39t}`WIl
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays ltkI}h,e
on the receiver to achieve uniform distribution. It is likely that you will need to k"g._|G
define more rays than 800 in a simulation in order to get 800 rays on the receiver. U| HB=BP
When using simplified meshes as merit functions, you should check the before and wZ4tCZA
after performance of a design to verify that the changes correlate to the changes of ]`bQW?
the merit function during optimization. As a design reaches its final performance GZ{]0$9I'
level, you will have to add rays to the simulation to reduce the noise floor so that H 33i*][H
sufficient accuracy and mesh resolution are available for the optimizer to find the L{E^?i X
best solution.
