| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? Wc#:f 8dr
While it is important to ensure that a sufficient number of rays are traced to H^CilwD158
distinguish the merit function value from the noise floor, it is often not necessary to nY) .|\|i
trace as many rays during optimization as you might to obtain a given level of Gbd?%{Xc-
accuracy for analysis purposes. What matters during optimization is that the j/<??v4F4
changes the optimizer makes to the model affect the merit function in the same way K a r~I
that the overall performance is affected. It is possible to define the merit function so 1BD6 l2y
that it has less accuracy and/or coarser mesh resolution than meshes used for !J.rM5K
analysis and yet produce improvements during optimization, especially in the early ,p, Du
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stages of a design. co B 6 rW
A rule of thumb for the first Monte Carlo run on a system is to have an average of at r2G*!qK*1
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays | Od u4 Q
on the receiver to achieve uniform distribution. It is likely that you will need to Yn[x #DS
define more rays than 800 in a simulation in order to get 800 rays on the receiver. z=?0)e(H,
When using simplified meshes as merit functions, you should check the before and W<gD6+=8
after performance of a design to verify that the changes correlate to the changes of /.Wc_/
the merit function during optimization. As a design reaches its final performance x9`ZO< L$
level, you will have to add rays to the simulation to reduce the noise floor so that h1%y:[_
sufficient accuracy and mesh resolution are available for the optimizer to find the (4)3W^/kk?
best solution.
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