How Many Rays Do I Need for Monte Carlo Optimization? > R=YF* t
While it is important to ensure that a sufficient number of rays are traced to :Kiu*&{
distinguish the merit function value from the noise floor, it is often not necessary to d@ hJ=-4
trace as many rays during optimization as you might to obtain a given level of zYgLGwi{
accuracy for analysis purposes. What matters during optimization is that the
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changes the optimizer makes to the model affect the merit function in the same way STe;Sr&p
that the overall performance is affected. It is possible to define the merit function so wal }[F#
that it has less accuracy and/or coarser mesh resolution than meshes used for ^-ZqS
analysis and yet produce improvements during optimization, especially in the early _qV_(TpS+
stages of a design. A\` Uu&
A rule of thumb for the first Monte Carlo run on a system is to have an average of at )1/O_N6C
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays Lst 5
on the receiver to achieve uniform distribution. It is likely that you will need to _wBPn6gg`
define more rays than 800 in a simulation in order to get 800 rays on the receiver. ^d,d<Uc
When using simplified meshes as merit functions, you should check the before and ?W()Do1tR
after performance of a design to verify that the changes correlate to the changes of *RPI$0
the merit function during optimization. As a design reaches its final performance
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level, you will have to add rays to the simulation to reduce the noise floor so that V6Y!0,w!a
sufficient accuracy and mesh resolution are available for the optimizer to find the *3|K bCX
best solution.
