How Many Rays Do I Need for Monte Carlo Optimization? gt~u/Z%
While it is important to ensure that a sufficient number of rays are traced to f^IB:e#j;
distinguish the merit function value from the noise floor, it is often not necessary to j|p=JrCJ
trace as many rays during optimization as you might to obtain a given level of ``{GU }n
accuracy for analysis purposes. What matters during optimization is that the /,$V/q+
changes the optimizer makes to the model affect the merit function in the same way l>(*bb1}b
that the overall performance is affected. It is possible to define the merit function so Z;0<k;#T(p
that it has less accuracy and/or coarser mesh resolution than meshes used for i`iR7UmHeR
analysis and yet produce improvements during optimization, especially in the early W58%Zz4a
stages of a design. cqm:[0Xf5>
A rule of thumb for the first Monte Carlo run on a system is to have an average of at L(1} PZ
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays e$rPXRf
on the receiver to achieve uniform distribution. It is likely that you will need to \E05qk_;K
define more rays than 800 in a simulation in order to get 800 rays on the receiver. U5?QneK
When using simplified meshes as merit functions, you should check the before and 5HY0 *\
after performance of a design to verify that the changes correlate to the changes of G}}Lp~
the merit function during optimization. As a design reaches its final performance xv%]g= Q
level, you will have to add rays to the simulation to reduce the noise floor so that gieso f
sufficient accuracy and mesh resolution are available for the optimizer to find the 3&+ nV1
best solution.
