How Many Rays Do I Need for Monte Carlo Optimization? Me Ea| .
While it is important to ensure that a sufficient number of rays are traced to rv*{[K
distinguish the merit function value from the noise floor, it is often not necessary to )}@D\(/@
trace as many rays during optimization as you might to obtain a given level of )j36Y =r3
accuracy for analysis purposes. What matters during optimization is that the #KJ # 1
changes the optimizer makes to the model affect the merit function in the same way *(OG +OkC
that the overall performance is affected. It is possible to define the merit function so ?.46X^
that it has less accuracy and/or coarser mesh resolution than meshes used for @s L N
analysis and yet produce improvements during optimization, especially in the early =OH X5:Z
stages of a design. ;
j!dbT~5
A rule of thumb for the first Monte Carlo run on a system is to have an average of at '? 5-
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays 5^g*
on the receiver to achieve uniform distribution. It is likely that you will need to ,<Q
define more rays than 800 in a simulation in order to get 800 rays on the receiver. odhS0+d^
When using simplified meshes as merit functions, you should check the before and ,>a!CnK=
after performance of a design to verify that the changes correlate to the changes of loVg{N :
the merit function during optimization. As a design reaches its final performance m)tu~ neM
level, you will have to add rays to the simulation to reduce the noise floor so that > 3uNh:|>/
sufficient accuracy and mesh resolution are available for the optimizer to find the /mex{+p>tO
best solution.
