How Many Rays Do I Need for Monte Carlo Optimization? piu0^vEEH
While it is important to ensure that a sufficient number of rays are traced to uJPH~mdW
distinguish the merit function value from the noise floor, it is often not necessary to qWB%),`j>
trace as many rays during optimization as you might to obtain a given level of Bz]J=g7
accuracy for analysis purposes. What matters during optimization is that the >0T3 '/k<H
changes the optimizer makes to the model affect the merit function in the same way om7`w
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that the overall performance is affected. It is possible to define the merit function so MYTS3(
that it has less accuracy and/or coarser mesh resolution than meshes used for U,3d) ]Zy&
analysis and yet produce improvements during optimization, especially in the early .! j#3J..u
stages of a design. tQ0=p|
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A rule of thumb for the first Monte Carlo run on a system is to have an average of at lS 3 _Ild
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays B,0+HoP
on the receiver to achieve uniform distribution. It is likely that you will need to 3o7xN=N
define more rays than 800 in a simulation in order to get 800 rays on the receiver. \G=bj;&eF
When using simplified meshes as merit functions, you should check the before and l\U*sro<
after performance of a design to verify that the changes correlate to the changes of n1)'cS 5}
the merit function during optimization. As a design reaches its final performance Y:%" K
level, you will have to add rays to the simulation to reduce the noise floor so that T{~M iC6A
sufficient accuracy and mesh resolution are available for the optimizer to find the
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best solution.
