How Many Rays Do I Need for Monte Carlo Optimization? C%0 |o/Wi
While it is important to ensure that a sufficient number of rays are traced to
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distinguish the merit function value from the noise floor, it is often not necessary to u\.7# D>
trace as many rays during optimization as you might to obtain a given level of z^O>'9#
accuracy for analysis purposes. What matters during optimization is that the #8QQZdC8`
changes the optimizer makes to the model affect the merit function in the same way RT 4ns +J1
that the overall performance is affected. It is possible to define the merit function so f_7a) 'V4
that it has less accuracy and/or coarser mesh resolution than meshes used for v|"Nx42
analysis and yet produce improvements during optimization, especially in the early 5L%A5C&|
stages of a design. +m]$P,yMt
A rule of thumb for the first Monte Carlo run on a system is to have an average of at +t})tDPXw
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays *dL!)+:d
on the receiver to achieve uniform distribution. It is likely that you will need to H~e;S#3_v
define more rays than 800 in a simulation in order to get 800 rays on the receiver. -!k"*P
When using simplified meshes as merit functions, you should check the before and 8$BZbj%?hx
after performance of a design to verify that the changes correlate to the changes of =4 36/O`K
the merit function during optimization. As a design reaches its final performance p{[Ol
level, you will have to add rays to the simulation to reduce the noise floor so that e >=P'
sufficient accuracy and mesh resolution are available for the optimizer to find the A90o X1l
best solution.
