| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? dZ#&YG)?e
While it is important to ensure that a sufficient number of rays are traced to 5 @U<I
distinguish the merit function value from the noise floor, it is often not necessary to YHNR 3
trace as many rays during optimization as you might to obtain a given level of $rIoHxh. y
accuracy for analysis purposes. What matters during optimization is that the [.se|]t7X
changes the optimizer makes to the model affect the merit function in the same way X cr
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that the overall performance is affected. It is possible to define the merit function so K 0gI) :
that it has less accuracy and/or coarser mesh resolution than meshes used for ]i(-I <`
analysis and yet produce improvements during optimization, especially in the early SIO&rrT.
stages of a design. Y8M ] L wj
A rule of thumb for the first Monte Carlo run on a system is to have an average of at !+>v[(OzM
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays =4V&*go*\
on the receiver to achieve uniform distribution. It is likely that you will need to _Zk{!
define more rays than 800 in a simulation in order to get 800 rays on the receiver. 2M#M"LHo
When using simplified meshes as merit functions, you should check the before and glD cUCF3
after performance of a design to verify that the changes correlate to the changes of oSiMpQu08
the merit function during optimization. As a design reaches its final performance Lbe\@S
level, you will have to add rays to the simulation to reduce the noise floor so that &'cL%.
sufficient accuracy and mesh resolution are available for the optimizer to find the X%z }VA
best solution.
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