| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? +Ze} B*0
While it is important to ensure that a sufficient number of rays are traced to ic:zsuEm
distinguish the merit function value from the noise floor, it is often not necessary to ,)cM3 nu
trace as many rays during optimization as you might to obtain a given level of #~]zh HI
accuracy for analysis purposes. What matters during optimization is that the 4>
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changes the optimizer makes to the model affect the merit function in the same way !)f\%lb
that the overall performance is affected. It is possible to define the merit function so `7E;VL^Y1
that it has less accuracy and/or coarser mesh resolution than meshes used for , >a&"V^k
analysis and yet produce improvements during optimization, especially in the early "Fr.fhh'~
stages of a design. iQ67l\{R
A rule of thumb for the first Monte Carlo run on a system is to have an average of at e+7"/icK
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays [>I<#_^~
on the receiver to achieve uniform distribution. It is likely that you will need to (XTG8W sN
define more rays than 800 in a simulation in order to get 800 rays on the receiver. K8|r&`X0
When using simplified meshes as merit functions, you should check the before and /xBb[44z8
after performance of a design to verify that the changes correlate to the changes of Wu/]MBM
the merit function during optimization. As a design reaches its final performance 5vQHhwO50k
level, you will have to add rays to the simulation to reduce the noise floor so that RMV/&85?y
sufficient accuracy and mesh resolution are available for the optimizer to find the v4TQX<0s
best solution.
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