How Many Rays Do I Need for Monte Carlo Optimization? RZvRV?<bR
While it is important to ensure that a sufficient number of rays are traced to i;2V
distinguish the merit function value from the noise floor, it is often not necessary to 'p Aq;2AA
trace as many rays during optimization as you might to obtain a given level of 8LtkP&Wx
accuracy for analysis purposes. What matters during optimization is that the Ze`ms96j{
changes the optimizer makes to the model affect the merit function in the same way <.|]%7
that the overall performance is affected. It is possible to define the merit function so (i)O@Jve
that it has less accuracy and/or coarser mesh resolution than meshes used for CwF=@:*d
analysis and yet produce improvements during optimization, especially in the early 6.v)q ,JL
stages of a design. \n0Gr\:
A rule of thumb for the first Monte Carlo run on a system is to have an average of at _h B7;N3
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays B3u:D"t
on the receiver to achieve uniform distribution. It is likely that you will need to n%dh| j2u
define more rays than 800 in a simulation in order to get 800 rays on the receiver. btf]~YN
When using simplified meshes as merit functions, you should check the before and LZPLz@=&]
after performance of a design to verify that the changes correlate to the changes of \p iz Vt
the merit function during optimization. As a design reaches its final performance 7*&q"
level, you will have to add rays to the simulation to reduce the noise floor so that ()$tP3 o
sufficient accuracy and mesh resolution are available for the optimizer to find the L{1PCs36c
best solution.
