| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? 3v QVk
While it is important to ensure that a sufficient number of rays are traced to A8Ae M `
distinguish the merit function value from the noise floor, it is often not necessary to bX5/xf$q
trace as many rays during optimization as you might to obtain a given level of i3Xo6! Q
accuracy for analysis purposes. What matters during optimization is that the 9+.3GRt7
changes the optimizer makes to the model affect the merit function in the same way nvc(< Ovw
that the overall performance is affected. It is possible to define the merit function so Fta=yH }
that it has less accuracy and/or coarser mesh resolution than meshes used for r?pFc3 ~N
analysis and yet produce improvements during optimization, especially in the early TQ[J,
stages of a design. F< dhG>E9
A rule of thumb for the first Monte Carlo run on a system is to have an average of at uBC#4cX`D*
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays y`/:E<fVk
on the receiver to achieve uniform distribution. It is likely that you will need to d?cCSf
define more rays than 800 in a simulation in order to get 800 rays on the receiver. R?}%rP+^e
When using simplified meshes as merit functions, you should check the before and jx Yze/I
after performance of a design to verify that the changes correlate to the changes of T$;BZ=_
the merit function during optimization. As a design reaches its final performance /N./l4D1K-
level, you will have to add rays to the simulation to reduce the noise floor so that c{x:'@%/s'
sufficient accuracy and mesh resolution are available for the optimizer to find the % /!f^PIwX
best solution.
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