| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? L5|;VH
While it is important to ensure that a sufficient number of rays are traced to ?;7>`F6ld
distinguish the merit function value from the noise floor, it is often not necessary to bzL;)H4Eo
trace as many rays during optimization as you might to obtain a given level of iW%0pLn
accuracy for analysis purposes. What matters during optimization is that the h] TVi$J
changes the optimizer makes to the model affect the merit function in the same way dE!=a|Pl
that the overall performance is affected. It is possible to define the merit function so ?@BaBU:o`F
that it has less accuracy and/or coarser mesh resolution than meshes used for T`0gtS S
analysis and yet produce improvements during optimization, especially in the early JRs[%w`kD
stages of a design. 8[P6c;\
A rule of thumb for the first Monte Carlo run on a system is to have an average of at GM5 6xZ!2T
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays r\- k/ 0
on the receiver to achieve uniform distribution. It is likely that you will need to f6A['<%o
define more rays than 800 in a simulation in order to get 800 rays on the receiver. -hV KPIb
When using simplified meshes as merit functions, you should check the before and z{+; ' 9C
after performance of a design to verify that the changes correlate to the changes of k#G 7`dJl
the merit function during optimization. As a design reaches its final performance -r0 \
level, you will have to add rays to the simulation to reduce the noise floor so that y/*Tvb #TJ
sufficient accuracy and mesh resolution are available for the optimizer to find the HQj4h]O#
best solution.
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