| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? O5r8Ghf )
While it is important to ensure that a sufficient number of rays are traced to J>v[5FX+
distinguish the merit function value from the noise floor, it is often not necessary to
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trace as many rays during optimization as you might to obtain a given level of Z
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accuracy for analysis purposes. What matters during optimization is that the ]t)N3n6Bc
changes the optimizer makes to the model affect the merit function in the same way QwX81*n x
that the overall performance is affected. It is possible to define the merit function so D`@a*YIq
that it has less accuracy and/or coarser mesh resolution than meshes used for {j$2=0Cec
analysis and yet produce improvements during optimization, especially in the early o6A$)m5V
stages of a design. Nqj@p<y/q
A rule of thumb for the first Monte Carlo run on a system is to have an average of at b3%x&H<j
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays t[TM\j0jW
on the receiver to achieve uniform distribution. It is likely that you will need to 6agq ^wI
define more rays than 800 in a simulation in order to get 800 rays on the receiver. -7&ywgxl
When using simplified meshes as merit functions, you should check the before and h |]cZMGo
after performance of a design to verify that the changes correlate to the changes of ow \EL
the merit function during optimization. As a design reaches its final performance \=WPJm`p
level, you will have to add rays to the simulation to reduce the noise floor so that `R2Iw
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sufficient accuracy and mesh resolution are available for the optimizer to find the r\"R?P$y|
best solution.
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