How Many Rays Do I Need for Monte Carlo Optimization? (xk.NZn F
While it is important to ensure that a sufficient number of rays are traced to #Acon7R p
distinguish the merit function value from the noise floor, it is often not necessary to =F'l's^j
trace as many rays during optimization as you might to obtain a given level of bc 3|;O
accuracy for analysis purposes. What matters during optimization is that the hw&ke$Fg#
changes the optimizer makes to the model affect the merit function in the same way b{~fVil$y
that the overall performance is affected. It is possible to define the merit function so ]k[ Q] :q
that it has less accuracy and/or coarser mesh resolution than meshes used for k%EWkM )?
analysis and yet produce improvements during optimization, especially in the early ntrY =Y
stages of a design. 7.wR"1p#
A rule of thumb for the first Monte Carlo run on a system is to have an average of at #d }0}7ue
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays }G>v]bV0V
on the receiver to achieve uniform distribution. It is likely that you will need to ;yk9(wea}"
define more rays than 800 in a simulation in order to get 800 rays on the receiver. jgk{' _ j
When using simplified meshes as merit functions, you should check the before and B,~f "
after performance of a design to verify that the changes correlate to the changes of Ez zTJ>
the merit function during optimization. As a design reaches its final performance dIoF ~8V
level, you will have to add rays to the simulation to reduce the noise floor so that /~H[= Pf
sufficient accuracy and mesh resolution are available for the optimizer to find the fkdf~Vb
best solution.
