| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? !<b+7 A
While it is important to ensure that a sufficient number of rays are traced to 8p1:dTI5Pb
distinguish the merit function value from the noise floor, it is often not necessary to :R$v7{1
trace as many rays during optimization as you might to obtain a given level of t^%)d7$
accuracy for analysis purposes. What matters during optimization is that the w]N;HlU
changes the optimizer makes to the model affect the merit function in the same way .f!:@fX>=
that the overall performance is affected. It is possible to define the merit function so m"AyO"}I5
that it has less accuracy and/or coarser mesh resolution than meshes used for &?}h)U#:
analysis and yet produce improvements during optimization, especially in the early RK)ikLgp
stages of a design. l-Dg m
A rule of thumb for the first Monte Carlo run on a system is to have an average of at gT,iH.
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays ]I;owk,
on the receiver to achieve uniform distribution. It is likely that you will need to t_(S e
define more rays than 800 in a simulation in order to get 800 rays on the receiver. >N}+O<Fc
When using simplified meshes as merit functions, you should check the before and 0TiDQ4}i[
after performance of a design to verify that the changes correlate to the changes of 8&bNI@:@
the merit function during optimization. As a design reaches its final performance ;$qc@)Uwp
level, you will have to add rays to the simulation to reduce the noise floor so that #sbW^Q'I
sufficient accuracy and mesh resolution are available for the optimizer to find the yHZ&5
best solution.
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