How Many Rays Do I Need for Monte Carlo Optimization? }>>1<P<8-
While it is important to ensure that a sufficient number of rays are traced to ]V^iN=(_5
distinguish the merit function value from the noise floor, it is often not necessary to "W6uV!
trace as many rays during optimization as you might to obtain a given level of J!c)s!`w
accuracy for analysis purposes. What matters during optimization is that the
1eS&&J5
changes the optimizer makes to the model affect the merit function in the same way 3{'Ne}5%I
that the overall performance is affected. It is possible to define the merit function so >_[ 9t
that it has less accuracy and/or coarser mesh resolution than meshes used for 4!Fo$9
analysis and yet produce improvements during optimization, especially in the early |iak z|])
stages of a design. [xSF 6
A rule of thumb for the first Monte Carlo run on a system is to have an average of at )
i ;1*jK
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays u+Y\6~=+
on the receiver to achieve uniform distribution. It is likely that you will need to q]T1dz ?
define more rays than 800 in a simulation in order to get 800 rays on the receiver. _Gn2o2T
When using simplified meshes as merit functions, you should check the before and Q-_N2W ?
after performance of a design to verify that the changes correlate to the changes of QoI3>Oj=
the merit function during optimization. As a design reaches its final performance ^uKwB;@
level, you will have to add rays to the simulation to reduce the noise floor so that ')) 0Lh
l
sufficient accuracy and mesh resolution are available for the optimizer to find the k.uH~S _
best solution.
