How Many Rays Do I Need for Monte Carlo Optimization? cq,0?2R`t
While it is important to ensure that a sufficient number of rays are traced to w`$ M}oX(
distinguish the merit function value from the noise floor, it is often not necessary to +__PT4ps
trace as many rays during optimization as you might to obtain a given level of c_#+xGS!7
accuracy for analysis purposes. What matters during optimization is that the w( ^
changes the optimizer makes to the model affect the merit function in the same way IDIok~B=e
that the overall performance is affected. It is possible to define the merit function so J@vL,C)E6
that it has less accuracy and/or coarser mesh resolution than meshes used for ` oq][|
analysis and yet produce improvements during optimization, especially in the early 3pF7} P
stages of a design. #7}1W[y9}l
A rule of thumb for the first Monte Carlo run on a system is to have an average of at #{BHH;J+
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays LnZC)cL
P/
on the receiver to achieve uniform distribution. It is likely that you will need to ;mAlF>6]\
define more rays than 800 in a simulation in order to get 800 rays on the receiver. *l T: P-
When using simplified meshes as merit functions, you should check the before and )Z0bM O<
after performance of a design to verify that the changes correlate to the changes of j aEU z5
the merit function during optimization. As a design reaches its final performance KtO|14 R:
level, you will have to add rays to the simulation to reduce the noise floor so that HDY2<Hzc
sufficient accuracy and mesh resolution are available for the optimizer to find the rV0X*[]J>
best solution.
