| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? ;iJ}[HUo
While it is important to ensure that a sufficient number of rays are traced to _Y$v=!fY&
distinguish the merit function value from the noise floor, it is often not necessary to Mc,p]{<<AV
trace as many rays during optimization as you might to obtain a given level of uax kGEXr
accuracy for analysis purposes. What matters during optimization is that the 9*Fc+/
changes the optimizer makes to the model affect the merit function in the same way 07:h4beT
that the overall performance is affected. It is possible to define the merit function so ldc`Y/:{
that it has less accuracy and/or coarser mesh resolution than meshes used for {7q8@`Oa
analysis and yet produce improvements during optimization, especially in the early R;uP^
stages of a design. f9hH{ ( A
A rule of thumb for the first Monte Carlo run on a system is to have an average of at l1%*L yD
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays 9 WHarv2 @
on the receiver to achieve uniform distribution. It is likely that you will need to \lyHQ-gWhc
define more rays than 800 in a simulation in order to get 800 rays on the receiver. jO`L:D/C
When using simplified meshes as merit functions, you should check the before and z5sKV7&\[n
after performance of a design to verify that the changes correlate to the changes of };*&;GFe
the merit function during optimization. As a design reaches its final performance M?kXzb \O
level, you will have to add rays to the simulation to reduce the noise floor so that -Byl~n3*D
sufficient accuracy and mesh resolution are available for the optimizer to find the E.^u:0:P
best solution.
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