| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? OI0#@_L&
While it is important to ensure that a sufficient number of rays are traced to Ay\=&4dv
distinguish the merit function value from the noise floor, it is often not necessary to W>3[+wB
trace as many rays during optimization as you might to obtain a given level of d,kh6'g2@
accuracy for analysis purposes. What matters during optimization is that the e~]3/ 0
changes the optimizer makes to the model affect the merit function in the same way BoQLjS{kN
that the overall performance is affected. It is possible to define the merit function so bH4'j/3
that it has less accuracy and/or coarser mesh resolution than meshes used for *Kj*| >)
analysis and yet produce improvements during optimization, especially in the early &4 Py
stages of a design. `x%v& >
A rule of thumb for the first Monte Carlo run on a system is to have an average of at sq
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least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays +>3XJlZV
on the receiver to achieve uniform distribution. It is likely that you will need to xCU^4DO3p
define more rays than 800 in a simulation in order to get 800 rays on the receiver. ZC}'! $r7
When using simplified meshes as merit functions, you should check the before and : pj 00
after performance of a design to verify that the changes correlate to the changes of lb M)U
the merit function during optimization. As a design reaches its final performance _Iz JxAcJ
level, you will have to add rays to the simulation to reduce the noise floor so that sf{rs*bgp
sufficient accuracy and mesh resolution are available for the optimizer to find the ?d@3y<A,~
best solution.
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