| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? Ug~ ]! L
While it is important to ensure that a sufficient number of rays are traced to ]A]EED.ZH
distinguish the merit function value from the noise floor, it is often not necessary to 'U|Tye i?
trace as many rays during optimization as you might to obtain a given level of ,T<q"d7-#
accuracy for analysis purposes. What matters during optimization is that the gOSFvH8FU
changes the optimizer makes to the model affect the merit function in the same way D>>?8a
that the overall performance is affected. It is possible to define the merit function so 1SY`V?cu
that it has less accuracy and/or coarser mesh resolution than meshes used for jSKhWxL;'
analysis and yet produce improvements during optimization, especially in the early G Ch]5\
stages of a design. J =j6rD
A rule of thumb for the first Monte Carlo run on a system is to have an average of at Oh]RIWL
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays m R|;}u;d
on the receiver to achieve uniform distribution. It is likely that you will need to -w3KBlo
define more rays than 800 in a simulation in order to get 800 rays on the receiver. Q.zE}ZS
When using simplified meshes as merit functions, you should check the before and NKX62 ZC
after performance of a design to verify that the changes correlate to the changes of +yO^,{8SE
the merit function during optimization. As a design reaches its final performance 'G>gNq
level, you will have to add rays to the simulation to reduce the noise floor so that s ~ Xa=_+D
sufficient accuracy and mesh resolution are available for the optimizer to find the 67]kT%0
best solution.
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