How Many Rays Do I Need for Monte Carlo Optimization? t `)
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While it is important to ensure that a sufficient number of rays are traced to 8 f|9W%jt
distinguish the merit function value from the noise floor, it is often not necessary to Pkj T&e)
trace as many rays during optimization as you might to obtain a given level of $U\!q@'$
accuracy for analysis purposes. What matters during optimization is that the z w K g
changes the optimizer makes to the model affect the merit function in the same way (H'_KPK
that the overall performance is affected. It is possible to define the merit function so \a\^(`3a[
that it has less accuracy and/or coarser mesh resolution than meshes used for Hf;RIl2F
analysis and yet produce improvements during optimization, especially in the early " vv$%^
stages of a design. M4R%Gr,L a
A rule of thumb for the first Monte Carlo run on a system is to have an average of at 6-D%)Z(
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays D WsCYo
on the receiver to achieve uniform distribution. It is likely that you will need to w2.qT+; v
define more rays than 800 in a simulation in order to get 800 rays on the receiver. 8 [vl3C
When using simplified meshes as merit functions, you should check the before and @>d&5}F_>{
after performance of a design to verify that the changes correlate to the changes of }]uB?
+c
the merit function during optimization. As a design reaches its final performance @ARAX\ F
level, you will have to add rays to the simulation to reduce the noise floor so that H Jnv'^yn
sufficient accuracy and mesh resolution are available for the optimizer to find the 'SsPx&)l
best solution.
