How Many Rays Do I Need for Monte Carlo Optimization? lZRO"[<
While it is important to ensure that a sufficient number of rays are traced to j~Pw t9G
distinguish the merit function value from the noise floor, it is often not necessary to 2'=)ese
trace as many rays during optimization as you might to obtain a given level of Vj4 h#NN$
accuracy for analysis purposes. What matters during optimization is that the w-JWMgY8w
changes the optimizer makes to the model affect the merit function in the same way n@tt.n!{l
that the overall performance is affected. It is possible to define the merit function so 1|8Bv0-b
that it has less accuracy and/or coarser mesh resolution than meshes used for m7i_ Iv
analysis and yet produce improvements during optimization, especially in the early ^[SW07o~
stages of a design. \%r0 '1f
A rule of thumb for the first Monte Carlo run on a system is to have an average of at Y7 +c/co
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays f tM lm_u
on the receiver to achieve uniform distribution. It is likely that you will need to g"" 1\rc=
define more rays than 800 in a simulation in order to get 800 rays on the receiver. 8nBYP+t,e
When using simplified meshes as merit functions, you should check the before and %J-:%i
after performance of a design to verify that the changes correlate to the changes of I(7 GVYM
the merit function during optimization. As a design reaches its final performance ,s So\%
level, you will have to add rays to the simulation to reduce the noise floor so that R"XycXn_$
sufficient accuracy and mesh resolution are available for the optimizer to find the W*s=No3C
best solution.
