How Many Rays Do I Need for Monte Carlo Optimization? F_zs"ex/
While it is important to ensure that a sufficient number of rays are traced to 3@KX| -
distinguish the merit function value from the noise floor, it is often not necessary to !(w\%$|
trace as many rays during optimization as you might to obtain a given level of ;-n+=@]7
accuracy for analysis purposes. What matters during optimization is that the ZR6KE_
changes the optimizer makes to the model affect the merit function in the same way \2:
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that the overall performance is affected. It is possible to define the merit function so -"\z|OQ
that it has less accuracy and/or coarser mesh resolution than meshes used for ;wp)E nF
analysis and yet produce improvements during optimization, especially in the early }7X85@jC
stages of a design. /tJJ2 =%l
A rule of thumb for the first Monte Carlo run on a system is to have an average of at fJd!;ur)0
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays %z`b u2
on the receiver to achieve uniform distribution. It is likely that you will need to OY51~#BF
define more rays than 800 in a simulation in order to get 800 rays on the receiver. jToA"udW/
When using simplified meshes as merit functions, you should check the before and 3vHEPm]
after performance of a design to verify that the changes correlate to the changes of +<Uc42i7n
the merit function during optimization. As a design reaches its final performance Pd%o6~_*
level, you will have to add rays to the simulation to reduce the noise floor so that +<"s C+2
sufficient accuracy and mesh resolution are available for the optimizer to find the }a'8lwF%I
best solution.
