How Many Rays Do I Need for Monte Carlo Optimization? `*6 |2
While it is important to ensure that a sufficient number of rays are traced to DL`8qJ'mJs
distinguish the merit function value from the noise floor, it is often not necessary to p]0`rf!|
trace as many rays during optimization as you might to obtain a given level of hjyM xg;Q?
accuracy for analysis purposes. What matters during optimization is that the }{y)a< `
changes the optimizer makes to the model affect the merit function in the same way KRz~3yH{ c
that the overall performance is affected. It is possible to define the merit function so {]2^b )
that it has less accuracy and/or coarser mesh resolution than meshes used for 0<7sM#sI!
analysis and yet produce improvements during optimization, especially in the early d a< > a
stages of a design. `WIZY33V
A rule of thumb for the first Monte Carlo run on a system is to have an average of at \3OEC`
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays ~UJ.A<>Fh
on the receiver to achieve uniform distribution. It is likely that you will need to ~7 `,}) d
define more rays than 800 in a simulation in order to get 800 rays on the receiver. "AU.Eh"-1
When using simplified meshes as merit functions, you should check the before and -0UR%R7q
after performance of a design to verify that the changes correlate to the changes of R2v9gz;W
the merit function during optimization. As a design reaches its final performance >TMd1? ,
level, you will have to add rays to the simulation to reduce the noise floor so that ]DKRug5
sufficient accuracy and mesh resolution are available for the optimizer to find the 9}%$j
best solution.
