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6.10 OTHER NUMERICAL METHODS While the MoM-based GSM approach has been well established and accurate, other numerical approaches also exist for analyzing multilayer arrays. In particular, the finite element frequency-domain (FEFD) approach [11] and the finite difference time-domain (FDTD) approach [12] are gaining popularity for analyzing infinite periodic arrays. These approaches utilize periodic radiation conditions and periodic side-wall conditions to model infinite arrays under Floquet excitations. A unit cell is filled with a number of node points. The FEFD and FDTD methods express Maxwell s equations in terms of difference equations and solve the electromagnetic field components on these nodes inside a unit cell. These methods have natural abilities to handle dielectric inhomogeneity and anisotropy in all directions within a unit cell. However, both methods demand extensive computer capacity in regard to electromagnetic simulation of a structure.

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This method was rst proposed by [PLA 85, PLA 91] for stochastic automata networks (SAN), a model equivalent to the composition of Markov chains Then, it was extended to synchronous composition of generalized stochastic Petri nets (GSPN) by [DON 94] (ie with transitions fusion) The main advantage of the GSPN model is the factorization of synchronized transitions of the Markov chain on the net transitions, reducing signi cantly the size of the tensorial expression Whatever the application model, the Cartesian product of the subspaces is always a super-space of the state space, which minimizes complexity savings (in a substantial way in worst cases) To apply the tensorial method to asynchronous composition of nets (through place fusion), each subnet must have an abstract view of the other subnets [BUC 93] to build nite subspaces.

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Following this idea, we include the state space in a union of Cartesian products of subspaces, which leads to a decomposition of the generator in blocks of submatrices where each submatrix is de ned by a tensor expression [CAM 97] This decomposition bene ts from tensor algebra at the submatrix level and allows us to cope with the ratio of the state space and its super-space We have the same decomposition of the state space of a stochastic Petri net with phase-type distribution (PH-SPN) between markings and descriptors of the residual distributions [DON 98] In this case, we show that the state space is exactly the union of the Cartesian products This result leads us to regard PH-SPN as one of the most ef cient solutions for modeling general distributions After presenting the tensor analysis of stochastic Petri nets in section 11.

2 Fcas(n 1) + 10(Fcas,n Gcas(n 1) )/5

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3, we study in the last section the combination of aggregation and tensor decomposition with the help of the stochastic well-formed Petri net model The two approaches are not orthogonal and two dif culties must be overcome before using them simultaneously On the one hand, modeling the system may force the designer to choose between a stochastic well-formed Petri net (SWN) and a SPN composition On the other hand, if the modeling comes out on SWN composition, most often the tensor composition of the aggregated chains of the SWN is not an aggregation of the chain of the composed SWN Therefore, the authors of this chapter have demonstrated suf cient syntactic conditions for asynchronous and synchronous composition of SWNs For simplicity, we restrict ourselves to calculating steady-state distributions of the models However, the applicability of these methods usually extends to transient analysis.

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