How Many Rays Do I Need for Monte Carlo Optimization? 1^LdYO?g'
While it is important to ensure that a sufficient number of rays are traced to KF
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distinguish the merit function value from the noise floor, it is often not necessary to ele@xl
trace as many rays during optimization as you might to obtain a given level of o!:Z?.!
accuracy for analysis purposes. What matters during optimization is that the )w0x{_
changes the optimizer makes to the model affect the merit function in the same way kN.;;HFq#
that the overall performance is affected. It is possible to define the merit function so j1 KNgAo<4
that it has less accuracy and/or coarser mesh resolution than meshes used for kL%ot<rt)w
analysis and yet produce improvements during optimization, especially in the early I<O$);DV'
stages of a design. ._^}M<o L
A rule of thumb for the first Monte Carlo run on a system is to have an average of at yI 2UmhA
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays T/\RViG3
on the receiver to achieve uniform distribution. It is likely that you will need to rw,Ylr :3
define more rays than 800 in a simulation in order to get 800 rays on the receiver. Xd=KBB[r?
When using simplified meshes as merit functions, you should check the before and 0K[]UU= P=
after performance of a design to verify that the changes correlate to the changes of
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the merit function during optimization. As a design reaches its final performance /kY9z~l
level, you will have to add rays to the simulation to reduce the noise floor so that sSZ)C|Q
sufficient accuracy and mesh resolution are available for the optimizer to find the ?>Sv_ 0
best solution.
