| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? `)=A !x y
While it is important to ensure that a sufficient number of rays are traced to f'6qJk%J
distinguish the merit function value from the noise floor, it is often not necessary to jUJT cL
trace as many rays during optimization as you might to obtain a given level of SXBQ
accuracy for analysis purposes. What matters during optimization is that the ~26s7S}
changes the optimizer makes to the model affect the merit function in the same way c >
mu)('U
that the overall performance is affected. It is possible to define the merit function so _A,-[*OKI
that it has less accuracy and/or coarser mesh resolution than meshes used for lii ]4k+z
analysis and yet produce improvements during optimization, especially in the early Stw+Dm\!
stages of a design. VyoE5 o
A rule of thumb for the first Monte Carlo run on a system is to have an average of at `@$"L/AJ
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays 85|95P. <
on the receiver to achieve uniform distribution. It is likely that you will need to $}^\=p}X
define more rays than 800 in a simulation in order to get 800 rays on the receiver. F7Dc!JNa
When using simplified meshes as merit functions, you should check the before and P10p<@?
after performance of a design to verify that the changes correlate to the changes of %kZ~xbY
the merit function during optimization. As a design reaches its final performance rX! +@>4_L
level, you will have to add rays to the simulation to reduce the noise floor so that }^pQbFku
sufficient accuracy and mesh resolution are available for the optimizer to find the X(!AI|6Bt
best solution.
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