How Many Rays Do I Need for Monte Carlo Optimization? mvZ# FF1,J
While it is important to ensure that a sufficient number of rays are traced to %?PFe}
distinguish the merit function value from the noise floor, it is often not necessary to TMj;NSc3
trace as many rays during optimization as you might to obtain a given level of _/I">/ivlM
accuracy for analysis purposes. What matters during optimization is that the ] c7 X~y
changes the optimizer makes to the model affect the merit function in the same way _&#{cCo:
that the overall performance is affected. It is possible to define the merit function so bu]"?bc
that it has less accuracy and/or coarser mesh resolution than meshes used for H T Or
analysis and yet produce improvements during optimization, especially in the early qy3@>
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stages of a design. a9.yuSzL
A rule of thumb for the first Monte Carlo run on a system is to have an average of at Y/FPkH4
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays 8 XB[CbO
on the receiver to achieve uniform distribution. It is likely that you will need to r KrHd
define more rays than 800 in a simulation in order to get 800 rays on the receiver. RPW46l34
When using simplified meshes as merit functions, you should check the before and c;$ 4}U4
after performance of a design to verify that the changes correlate to the changes of LWF,w7v[L
the merit function during optimization. As a design reaches its final performance fu^W# "{
level, you will have to add rays to the simulation to reduce the noise floor so that cl%+m
sufficient accuracy and mesh resolution are available for the optimizer to find the HYfGu1j?X
best solution.
