How Many Rays Do I Need for Monte Carlo Optimization? m Yx6JU*`
While it is important to ensure that a sufficient number of rays are traced to uqHI /4
distinguish the merit function value from the noise floor, it is often not necessary to zI7iZ"2a
trace as many rays during optimization as you might to obtain a given level of -|DBO0q
accuracy for analysis purposes. What matters during optimization is that the [gaB}aLn
changes the optimizer makes to the model affect the merit function in the same way P=<>H9p:o
that the overall performance is affected. It is possible to define the merit function so ()MUyW"S#`
that it has less accuracy and/or coarser mesh resolution than meshes used for bZ SaL^^(
analysis and yet produce improvements during optimization, especially in the early *";O_ :C!
stages of a design. d-{1>\-_
A rule of thumb for the first Monte Carlo run on a system is to have an average of at GMKY1{
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays P>n z8NRq
on the receiver to achieve uniform distribution. It is likely that you will need to DCP
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define more rays than 800 in a simulation in order to get 800 rays on the receiver. IY,n7x0d
When using simplified meshes as merit functions, you should check the before and oiRrpS\T.
after performance of a design to verify that the changes correlate to the changes of ,p' ;Xg6ez
the merit function during optimization. As a design reaches its final performance _T.T[%-&=
level, you will have to add rays to the simulation to reduce the noise floor so that "1wjh=@z
sufficient accuracy and mesh resolution are available for the optimizer to find the ?s5/
best solution.
