| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? NC;T( @
While it is important to ensure that a sufficient number of rays are traced to Cl{{H]QngX
distinguish the merit function value from the noise floor, it is often not necessary to sI4QI\*4
trace as many rays during optimization as you might to obtain a given level of ~6MMErSj
accuracy for analysis purposes. What matters during optimization is that the O/nqNQ?<
changes the optimizer makes to the model affect the merit function in the same way WEp s.]s
that the overall performance is affected. It is possible to define the merit function so d Z-Ny_@&
that it has less accuracy and/or coarser mesh resolution than meshes used for t3 K>\ :
analysis and yet produce improvements during optimization, especially in the early (+@faP
stages of a design. *:(1K %g
A rule of thumb for the first Monte Carlo run on a system is to have an average of at R:BBF9sK?
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays EJv! tyJ\[
on the receiver to achieve uniform distribution. It is likely that you will need to d {?)q
define more rays than 800 in a simulation in order to get 800 rays on the receiver. U:J /\-
When using simplified meshes as merit functions, you should check the before and ]m RF[b$
after performance of a design to verify that the changes correlate to the changes of pDP3 3`OFh
the merit function during optimization. As a design reaches its final performance F61 +n!%8
level, you will have to add rays to the simulation to reduce the noise floor so that ^sJ1 ^LT
sufficient accuracy and mesh resolution are available for the optimizer to find the E8+8{
#f;
best solution.
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