How Many Rays Do I Need for Monte Carlo Optimization? (Toq^+`c
While it is important to ensure that a sufficient number of rays are traced to wB>r (xQ'
distinguish the merit function value from the noise floor, it is often not necessary to Il.Ed-&62
trace as many rays during optimization as you might to obtain a given level of rw)kAe31
accuracy for analysis purposes. What matters during optimization is that the y$81Z q
changes the optimizer makes to the model affect the merit function in the same way .}')f;jH5<
that the overall performance is affected. It is possible to define the merit function so `MP|Ovns:H
that it has less accuracy and/or coarser mesh resolution than meshes used for +jC*'7 p@
analysis and yet produce improvements during optimization, especially in the early n ]+W 3[i
stages of a design. 4lKVY<
A rule of thumb for the first Monte Carlo run on a system is to have an average of at 8vk..!7n}
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays S]sk7
on the receiver to achieve uniform distribution. It is likely that you will need to |+ge8uu?C
define more rays than 800 in a simulation in order to get 800 rays on the receiver. w#i[ _
When using simplified meshes as merit functions, you should check the before and @5)
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after performance of a design to verify that the changes correlate to the changes of midsnG+jnf
the merit function during optimization. As a design reaches its final performance 27ck dyQx
level, you will have to add rays to the simulation to reduce the noise floor so that 1xf=_F0`&
sufficient accuracy and mesh resolution are available for the optimizer to find the ENh!N4vbO
best solution.
