| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? NBjeH tT
While it is important to ensure that a sufficient number of rays are traced to \?D R
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distinguish the merit function value from the noise floor, it is often not necessary to YD\]{,F|
trace as many rays during optimization as you might to obtain a given level of P}V=*g
accuracy for analysis purposes. What matters during optimization is that the |E TiLR=&
changes the optimizer makes to the model affect the merit function in the same way >E :<E'L
that the overall performance is affected. It is possible to define the merit function so N
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that it has less accuracy and/or coarser mesh resolution than meshes used for M"{uX
analysis and yet produce improvements during optimization, especially in the early H2;X
stages of a design. Z)pz,
A rule of thumb for the first Monte Carlo run on a system is to have an average of at 09 S6#; N&
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays
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on the receiver to achieve uniform distribution. It is likely that you will need to u=r`t(Z1H
define more rays than 800 in a simulation in order to get 800 rays on the receiver. ]"F5;p; y
When using simplified meshes as merit functions, you should check the before and "CQw/qZw
after performance of a design to verify that the changes correlate to the changes of f?56=& pHY
the merit function during optimization. As a design reaches its final performance ]JE TeZ^/
level, you will have to add rays to the simulation to reduce the noise floor so that at|g%$%
sufficient accuracy and mesh resolution are available for the optimizer to find the b.F^vv"]]
best solution.
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