How Many Rays Do I Need for Monte Carlo Optimization? Af2( 5]
While it is important to ensure that a sufficient number of rays are traced to dt]-,Y
distinguish the merit function value from the noise floor, it is often not necessary to 7t0=[i
trace as many rays during optimization as you might to obtain a given level of ]y '>=a|T
accuracy for analysis purposes. What matters during optimization is that the b94DJzL1z
changes the optimizer makes to the model affect the merit function in the same way $szqy?i 0?
that the overall performance is affected. It is possible to define the merit function so 3z?> j]
that it has less accuracy and/or coarser mesh resolution than meshes used for
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analysis and yet produce improvements during optimization, especially in the early I; |B.j
stages of a design. }@+0/W?\.
A rule of thumb for the first Monte Carlo run on a system is to have an average of at :U%W%
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays x~~|.C ,
on the receiver to achieve uniform distribution. It is likely that you will need to 7(8;t o6(
define more rays than 800 in a simulation in order to get 800 rays on the receiver. i$G@R %
When using simplified meshes as merit functions, you should check the before and x J8M6O8
after performance of a design to verify that the changes correlate to the changes of n
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the merit function during optimization. As a design reaches its final performance =s2*H8]
level, you will have to add rays to the simulation to reduce the noise floor so that 1~
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sufficient accuracy and mesh resolution are available for the optimizer to find the PiIpnoM
best solution.
