How Many Rays Do I Need for Monte Carlo Optimization? bb!cZ >Z
While it is important to ensure that a sufficient number of rays are traced to }_h2:^n
distinguish the merit function value from the noise floor, it is often not necessary to feT.d +Fd
trace as many rays during optimization as you might to obtain a given level of _53N uEM1
accuracy for analysis purposes. What matters during optimization is that the y:VY8a 4
changes the optimizer makes to the model affect the merit function in the same way )vD|VLV
that the overall performance is affected. It is possible to define the merit function so G8@LH
that it has less accuracy and/or coarser mesh resolution than meshes used for yC9~X='D
analysis and yet produce improvements during optimization, especially in the early %5 Zhq >
stages of a design. .tzQ
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A rule of thumb for the first Monte Carlo run on a system is to have an average of at ;*>':-4
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays l*|m( 7s
on the receiver to achieve uniform distribution. It is likely that you will need to [w}KjV/yi
define more rays than 800 in a simulation in order to get 800 rays on the receiver. xX\A& 9 m
When using simplified meshes as merit functions, you should check the before and hEfFMi=a`
after performance of a design to verify that the changes correlate to the changes of 3
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the merit function during optimization. As a design reaches its final performance QV_Ep8
level, you will have to add rays to the simulation to reduce the noise floor so that ^dR gYi"(A
sufficient accuracy and mesh resolution are available for the optimizer to find the I 7{
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best solution.
