How Many Rays Do I Need for Monte Carlo Optimization? lNz7u:U3
While it is important to ensure that a sufficient number of rays are traced to :y+2*lV
distinguish the merit function value from the noise floor, it is often not necessary to J*r *X.
trace as many rays during optimization as you might to obtain a given level of 6^V=?~a&z
accuracy for analysis purposes. What matters during optimization is that the eX?OYDDC0j
changes the optimizer makes to the model affect the merit function in the same way \MA+f~)9
that the overall performance is affected. It is possible to define the merit function so %>yG+Od5 Z
that it has less accuracy and/or coarser mesh resolution than meshes used for !02`t4Zc-
analysis and yet produce improvements during optimization, especially in the early q_Q/3rh
stages of a design. 4;w;'3zq
A rule of thumb for the first Monte Carlo run on a system is to have an average of at LzG%Z1 `
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays h`]Iy
on the receiver to achieve uniform distribution. It is likely that you will need to xR- % L
define more rays than 800 in a simulation in order to get 800 rays on the receiver. cA2V2S)
When using simplified meshes as merit functions, you should check the before and n D0K).=Q
after performance of a design to verify that the changes correlate to the changes of zpzK>DH(
the merit function during optimization. As a design reaches its final performance fFMlDg[];
level, you will have to add rays to the simulation to reduce the noise floor so that r(6 Y*<
sufficient accuracy and mesh resolution are available for the optimizer to find the "~#3& 3HVS
best solution.
