| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? }MR1^
While it is important to ensure that a sufficient number of rays are traced to `=#01YX[0
distinguish the merit function value from the noise floor, it is often not necessary to oMcK`%ydm
trace as many rays during optimization as you might to obtain a given level of ]DFXPV
accuracy for analysis purposes. What matters during optimization is that the JJV0R}z?TV
changes the optimizer makes to the model affect the merit function in the same way IUGz =%[
that the overall performance is affected. It is possible to define the merit function so K\[!SXg@
that it has less accuracy and/or coarser mesh resolution than meshes used for h :Xz UxL\
analysis and yet produce improvements during optimization, especially in the early gw+9x<e
stages of a design. {qKxz9.y
A rule of thumb for the first Monte Carlo run on a system is to have an average of at IM=bK U
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays e]ig!G]
on the receiver to achieve uniform distribution. It is likely that you will need to s/"&9F3
define more rays than 800 in a simulation in order to get 800 rays on the receiver. bLz *A-
When using simplified meshes as merit functions, you should check the before and ;;5Uwd'-
after performance of a design to verify that the changes correlate to the changes of A]`El8_t"
the merit function during optimization. As a design reaches its final performance ezhDcI_ T
level, you will have to add rays to the simulation to reduce the noise floor so that 6Dws,_UAZ4
sufficient accuracy and mesh resolution are available for the optimizer to find the `&M{c fp_
best solution.
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