| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? L:_GpZ_
While it is important to ensure that a sufficient number of rays are traced to Z'!i"Jzq|{
distinguish the merit function value from the noise floor, it is often not necessary to eD ,'M
trace as many rays during optimization as you might to obtain a given level of I
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accuracy for analysis purposes. What matters during optimization is that the V~
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changes the optimizer makes to the model affect the merit function in the same way `Iqh\oY8-
that the overall performance is affected. It is possible to define the merit function so %
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that it has less accuracy and/or coarser mesh resolution than meshes used for %}}?Y`/W )
analysis and yet produce improvements during optimization, especially in the early _zK
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stages of a design. 5kik+
A rule of thumb for the first Monte Carlo run on a system is to have an average of at `Fx+HIng,
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays X-y3CO:&@h
on the receiver to achieve uniform distribution. It is likely that you will need to X#<+D1P
define more rays than 800 in a simulation in order to get 800 rays on the receiver. ^e{]WH?
When using simplified meshes as merit functions, you should check the before and ' +f(9/
after performance of a design to verify that the changes correlate to the changes of j@s* hZ^J+
the merit function during optimization. As a design reaches its final performance hlBMRx49
level, you will have to add rays to the simulation to reduce the noise floor so that g-4j1yJV<
sufficient accuracy and mesh resolution are available for the optimizer to find the `>Ms7G9S~e
best solution.
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