How Many Rays Do I Need for Monte Carlo Optimization? _kLoDju%
While it is important to ensure that a sufficient number of rays are traced to /L Tyiiz6
distinguish the merit function value from the noise floor, it is often not necessary to # nhAW
trace as many rays during optimization as you might to obtain a given level of 9R3=h5Y
accuracy for analysis purposes. What matters during optimization is that the m"}G-#
changes the optimizer makes to the model affect the merit function in the same way GTe9 @ d
that the overall performance is affected. It is possible to define the merit function so b)@x@3"O
that it has less accuracy and/or coarser mesh resolution than meshes used for /_(Dq8^g@
analysis and yet produce improvements during optimization, especially in the early Zt=X
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stages of a design. a{,t@G
A rule of thumb for the first Monte Carlo run on a system is to have an average of at [J 3;U6
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays u'> CU
on the receiver to achieve uniform distribution. It is likely that you will need to s l 5wX
define more rays than 800 in a simulation in order to get 800 rays on the receiver. :gt wvM7/B
When using simplified meshes as merit functions, you should check the before and B!a nY} /U
after performance of a design to verify that the changes correlate to the changes of ?[">%^
the merit function during optimization. As a design reaches its final performance 1vb0G ;a;|
level, you will have to add rays to the simulation to reduce the noise floor so that D1 k]
sufficient accuracy and mesh resolution are available for the optimizer to find the $!@f{9 +
best solution.
