| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? dpZ7eJ
While it is important to ensure that a sufficient number of rays are traced to &[*_ -
distinguish the merit function value from the noise floor, it is often not necessary to 7E Y~5U/4
trace as many rays during optimization as you might to obtain a given level of 7oF`Os+U
accuracy for analysis purposes. What matters during optimization is that the nX5*pTfjL3
changes the optimizer makes to the model affect the merit function in the same way 0o At=S
that the overall performance is affected. It is possible to define the merit function so , 9|%
that it has less accuracy and/or coarser mesh resolution than meshes used for ^K@r!)We
analysis and yet produce improvements during optimization, especially in the early rRcfZZ~` M
stages of a design. u>& \@?(
A rule of thumb for the first Monte Carlo run on a system is to have an average of at [2 2IF
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays *I Gxa
on the receiver to achieve uniform distribution. It is likely that you will need to BGOI$ ,
define more rays than 800 in a simulation in order to get 800 rays on the receiver. {9;~xxTo
When using simplified meshes as merit functions, you should check the before and wuzz Wq
after performance of a design to verify that the changes correlate to the changes of
a[";K,
the merit function during optimization. As a design reaches its final performance dr~M yQ
level, you will have to add rays to the simulation to reduce the noise floor so that J }jK_
sufficient accuracy and mesh resolution are available for the optimizer to find the H !F'I)1
best solution.
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