How Many Rays Do I Need for Monte Carlo Optimization? w2O!M!1
While it is important to ensure that a sufficient number of rays are traced to xda;
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distinguish the merit function value from the noise floor, it is often not necessary to i`(^[h
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trace as many rays during optimization as you might to obtain a given level of ^E`(*J/o
accuracy for analysis purposes. What matters during optimization is that the ?YM4b5!3T
changes the optimizer makes to the model affect the merit function in the same way G.'+-v=\]
that the overall performance is affected. It is possible to define the merit function so RF!a//
that it has less accuracy and/or coarser mesh resolution than meshes used for ~rr 4o k
analysis and yet produce improvements during optimization, especially in the early 5qUTMT['T
stages of a design. XZNY4/ 25G
A rule of thumb for the first Monte Carlo run on a system is to have an average of at :q<Z'EnW
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays 8N%Bn&
on the receiver to achieve uniform distribution. It is likely that you will need to GV6K/ T :
define more rays than 800 in a simulation in order to get 800 rays on the receiver. Dq@2-Cv
When using simplified meshes as merit functions, you should check the before and c)md
after performance of a design to verify that the changes correlate to the changes of sAJ7R(p
the merit function during optimization. As a design reaches its final performance -tsDMji~V
level, you will have to add rays to the simulation to reduce the noise floor so that R
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sufficient accuracy and mesh resolution are available for the optimizer to find the
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best solution.
