| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? 2#'rk'X,K
While it is important to ensure that a sufficient number of rays are traced to &|/vM.
distinguish the merit function value from the noise floor, it is often not necessary to sk#9x`Rw
trace as many rays during optimization as you might to obtain a given level of '/ Cg*o/
accuracy for analysis purposes. What matters during optimization is that the :L]-'\y
changes the optimizer makes to the model affect the merit function in the same way 4 0 A&#u9o
that the overall performance is affected. It is possible to define the merit function so /r>IV`n{
that it has less accuracy and/or coarser mesh resolution than meshes used for +*n] tlk
analysis and yet produce improvements during optimization, especially in the early 6e,Apj 0
stages of a design. .JNcY]V#
A rule of thumb for the first Monte Carlo run on a system is to have an average of at .H
Fc9^.*
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays v B Sm=M
on the receiver to achieve uniform distribution. It is likely that you will need to ]AFj&CteZ/
define more rays than 800 in a simulation in order to get 800 rays on the receiver. IZ+ *`E
When using simplified meshes as merit functions, you should check the before and Po!oN~r
after performance of a design to verify that the changes correlate to the changes of 7Aqn[1{_O
the merit function during optimization. As a design reaches its final performance
T :c7@^=
level, you will have to add rays to the simulation to reduce the noise floor so that GNs#oM
sufficient accuracy and mesh resolution are available for the optimizer to find the ] F*|U`
best solution.
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