How Many Rays Do I Need for Monte Carlo Optimization? | wKZ-6
While it is important to ensure that a sufficient number of rays are traced to iO,0Sb
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distinguish the merit function value from the noise floor, it is often not necessary to x'+lNl v
trace as many rays during optimization as you might to obtain a given level of %=S~[&8C
accuracy for analysis purposes. What matters during optimization is that the 7y&Fb
changes the optimizer makes to the model affect the merit function in the same way Txj%o5G
that the overall performance is affected. It is possible to define the merit function so ,/C<GFae
that it has less accuracy and/or coarser mesh resolution than meshes used for mfIY7DP
analysis and yet produce improvements during optimization, especially in the early $e _A( |
stages of a design. ="P 3TP
A rule of thumb for the first Monte Carlo run on a system is to have an average of at 88$G14aXEk
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays \3hhM}6)DM
on the receiver to achieve uniform distribution. It is likely that you will need to `QC{}Oo^
define more rays than 800 in a simulation in order to get 800 rays on the receiver. 5qGRz"\p~
When using simplified meshes as merit functions, you should check the before and ^g$k4
after performance of a design to verify that the changes correlate to the changes of 1%G<gbHpI
the merit function during optimization. As a design reaches its final performance , pq<.?&E
level, you will have to add rays to the simulation to reduce the noise floor so that "gfy6m
sufficient accuracy and mesh resolution are available for the optimizer to find the #^" \WG7{
best solution.
