| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? 8B+^vF
While it is important to ensure that a sufficient number of rays are traced to 7:E#c"S
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distinguish the merit function value from the noise floor, it is often not necessary to `2,_"9Z(
trace as many rays during optimization as you might to obtain a given level of ?'m5)Z{
accuracy for analysis purposes. What matters during optimization is that the Riuv@i^6K
changes the optimizer makes to the model affect the merit function in the same way Awf = yE:
that the overall performance is affected. It is possible to define the merit function so @_ ZW P
that it has less accuracy and/or coarser mesh resolution than meshes used for 6v)eM=
analysis and yet produce improvements during optimization, especially in the early :?6$}GcW
stages of a design. vbh# [,lh
A rule of thumb for the first Monte Carlo run on a system is to have an average of at qA/ 3uA!z
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays [7w_.(f#
on the receiver to achieve uniform distribution. It is likely that you will need to pF RnPOv
define more rays than 800 in a simulation in order to get 800 rays on the receiver. T sW6 w
When using simplified meshes as merit functions, you should check the before and .h^Ld,Chj
after performance of a design to verify that the changes correlate to the changes of n8aiGnd=v
the merit function during optimization. As a design reaches its final performance bO3KaOC8N
level, you will have to add rays to the simulation to reduce the noise floor so that N ] /d
sufficient accuracy and mesh resolution are available for the optimizer to find the D I [^H
best solution.
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