| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? /K . !sQ$
While it is important to ensure that a sufficient number of rays are traced to D F* :_B )
distinguish the merit function value from the noise floor, it is often not necessary to ;{ U@qQD7
trace as many rays during optimization as you might to obtain a given level of :gep:4&u
accuracy for analysis purposes. What matters during optimization is that the 2(#7[mgPI
changes the optimizer makes to the model affect the merit function in the same way B Ere*J
that the overall performance is affected. It is possible to define the merit function so -@0GcUE: r
that it has less accuracy and/or coarser mesh resolution than meshes used for _N{RVeO
analysis and yet produce improvements during optimization, especially in the early N _8 6t
stages of a design. E .Jkf\
A rule of thumb for the first Monte Carlo run on a system is to have an average of at \uaJ @{Vug
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays K|:@Z
on the receiver to achieve uniform distribution. It is likely that you will need to .P7"e5g e
define more rays than 800 in a simulation in order to get 800 rays on the receiver. fmb} 2h
When using simplified meshes as merit functions, you should check the before and @T'i/}nl
after performance of a design to verify that the changes correlate to the changes of Q|D @Yd\
the merit function during optimization. As a design reaches its final performance `Ch9~* p
level, you will have to add rays to the simulation to reduce the noise floor so that 2 AO~HxF
sufficient accuracy and mesh resolution are available for the optimizer to find the $h*L=t(
best solution.
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