| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? (;~[}"
While it is important to ensure that a sufficient number of rays are traced to 8{%/!ylJz
distinguish the merit function value from the noise floor, it is often not necessary to @8n0GCv
trace as many rays during optimization as you might to obtain a given level of zr8 4%_^
accuracy for analysis purposes. What matters during optimization is that the +;FF0_
changes the optimizer makes to the model affect the merit function in the same way .Zf#L'Rf
that the overall performance is affected. It is possible to define the merit function so W
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that it has less accuracy and/or coarser mesh resolution than meshes used for AGK+~EjL@
analysis and yet produce improvements during optimization, especially in the early 6tzZ j:y q
stages of a design. () b0Sh=
A rule of thumb for the first Monte Carlo run on a system is to have an average of at MT%ky
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays AWD &K!
on the receiver to achieve uniform distribution. It is likely that you will need to }! zjj\g^
define more rays than 800 in a simulation in order to get 800 rays on the receiver. 1hi^
When using simplified meshes as merit functions, you should check the before and W=I%3F_C"R
after performance of a design to verify that the changes correlate to the changes of z7HC6{g% X
the merit function during optimization. As a design reaches its final performance g>OGh o
level, you will have to add rays to the simulation to reduce the noise floor so that k(%RX _]C
sufficient accuracy and mesh resolution are available for the optimizer to find the 7dV^35 KP
best solution.
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