How Many Rays Do I Need for Monte Carlo Optimization? (Lb bc+1m
While it is important to ensure that a sufficient number of rays are traced to 4S7v:1~xe
distinguish the merit function value from the noise floor, it is often not necessary to ))q y;Q,
trace as many rays during optimization as you might to obtain a given level of P9R9(quI
accuracy for analysis purposes. What matters during optimization is that the 0HZ{Y9]
changes the optimizer makes to the model affect the merit function in the same way })'B<vq
that the overall performance is affected. It is possible to define the merit function so b !+hH Hv:
that it has less accuracy and/or coarser mesh resolution than meshes used for Z3Og=XHR
analysis and yet produce improvements during optimization, especially in the early -{("mR&]
stages of a design. N:^n('U&j
A rule of thumb for the first Monte Carlo run on a system is to have an average of at AzPu)
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays y#`tgJ:
on the receiver to achieve uniform distribution. It is likely that you will need to &eJfGt5
define more rays than 800 in a simulation in order to get 800 rays on the receiver. @ JGP,445
When using simplified meshes as merit functions, you should check the before and F|`Hm
after performance of a design to verify that the changes correlate to the changes of R8K& R\
the merit function during optimization. As a design reaches its final performance W <'m: dq
level, you will have to add rays to the simulation to reduce the noise floor so that zOJ% }
sufficient accuracy and mesh resolution are available for the optimizer to find the \P[Y`LYL
best solution.
