| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? Yn IM-
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 3ZW/$KP/
trace as many rays during optimization as you might to obtain a given level of 'uP'P#
accuracy for analysis purposes. What matters during optimization is that the /@9-!cL
changes the optimizer makes to the model affect the merit function in the same way r+[#%%}ea
that the overall performance is affected. It is possible to define the merit function so <?> I\
that it has less accuracy and/or coarser mesh resolution than meshes used for nu4 69
analysis and yet produce improvements during optimization, especially in the early +X* F<6mZ
stages of a design. K)Df}fVOc
A rule of thumb for the first Monte Carlo run on a system is to have an average of at $I) Tk`=
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays sW ]yuu!/
on the receiver to achieve uniform distribution. It is likely that you will need to u|_LR5S!j
define more rays than 800 in a simulation in order to get 800 rays on the receiver. 4cXAT9
When using simplified meshes as merit functions, you should check the before and [Vrc:%J k
after performance of a design to verify that the changes correlate to the changes of 26\HV
the merit function during optimization. As a design reaches its final performance wo7N7R5
level, you will have to add rays to the simulation to reduce the noise floor so that N<L$gw+)$D
sufficient accuracy and mesh resolution are available for the optimizer to find the V9 +xL 1U#
best solution.
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