How Many Rays Do I Need for Monte Carlo Optimization? q$:7j5E
While it is important to ensure that a sufficient number of rays are traced to 'z; (Y*jb
distinguish the merit function value from the noise floor, it is often not necessary to <"5l< E
trace as many rays during optimization as you might to obtain a given level of =U3S"W %
accuracy for analysis purposes. What matters during optimization is that the ZLT?G
changes the optimizer makes to the model affect the merit function in the same way ~i"=:D
that the overall performance is affected. It is possible to define the merit function so reN\| ?0{
that it has less accuracy and/or coarser mesh resolution than meshes used for &SE}5ddC7
analysis and yet produce improvements during optimization, especially in the early ks0Q+YW
stages of a design. R^.PKT2E
A rule of thumb for the first Monte Carlo run on a system is to have an average of at -U /)y:k!%
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays KMj\A
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on the receiver to achieve uniform distribution. It is likely that you will need to t2o{=!$WH
define more rays than 800 in a simulation in order to get 800 rays on the receiver. CW+kK N
When using simplified meshes as merit functions, you should check the before and 9 8|sWI3 B
after performance of a design to verify that the changes correlate to the changes of X[o+Y@bc
the merit function during optimization. As a design reaches its final performance <R]m(
level, you will have to add rays to the simulation to reduce the noise floor so that w 0_P9g:
sufficient accuracy and mesh resolution are available for the optimizer to find the [7I b T:ph
best solution.
