| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? VD90JU]X<
While it is important to ensure that a sufficient number of rays are traced to
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distinguish the merit function value from the noise floor, it is often not necessary to ,R7RXpP7t
trace as many rays during optimization as you might to obtain a given level of j\\uW)ibG
accuracy for analysis purposes. What matters during optimization is that the O|mWQp^?q
changes the optimizer makes to the model affect the merit function in the same way y6P-:f/& *
that the overall performance is affected. It is possible to define the merit function so WxJ V
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that it has less accuracy and/or coarser mesh resolution than meshes used for *s%M!YM
analysis and yet produce improvements during optimization, especially in the early b\Mb6s
stages of a design. ayZWt| iHA
A rule of thumb for the first Monte Carlo run on a system is to have an average of at v@1f,d
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays 9`Y\ `F#}q
on the receiver to achieve uniform distribution. It is likely that you will need to }Sh3AH/
define more rays than 800 in a simulation in order to get 800 rays on the receiver. [<JY[o=
When using simplified meshes as merit functions, you should check the before and CTf39R|7_
after performance of a design to verify that the changes correlate to the changes of KN:V:8:J
the merit function during optimization. As a design reaches its final performance 'p&q}IO
level, you will have to add rays to the simulation to reduce the noise floor so that 4Jk[X>I~
sufficient accuracy and mesh resolution are available for the optimizer to find the h/NI5
best solution.
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