By Martin Simon

ISBN-10: 3658109920

ISBN-13: 9783658109929

ISBN-10: 3658109939

ISBN-13: 9783658109936

This monograph is worried with the research and numerical answer of a stochastic inverse anomaly detection challenge in electric impedance tomography (EIT). Martin Simon stories the matter of detecting a parameterized anomaly in an isotropic, desk bound and ergodic conductivity random box whose realizations are swiftly oscillating. For this goal, he derives Feynman-Kac formulae to scrupulously justify stochastic homogenization when it comes to the underlying stochastic boundary worth challenge. the writer combines ideas from the speculation of partial differential equations and useful research with probabilistic rules, paving how one can new mathematical theorems that could be fruitfully utilized in the remedy of the matter to hand. additionally, the writer proposes an effective numerical strategy within the framework of Bayesian inversion for the sensible answer of the stochastic inverse anomaly detection challenge.

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Extra resources for Anomaly Detection in Random Heterogeneous Media: Feynman-Kac Formulae, Stochastic Homogenization and Statistical Inversion

Example text

E. , where the second summand on the right-hand side is a local Px martingale. That is, there exists an increasing sequence (τk )k∈N of stopping times which tend to infinity such that for every k ∈ N t∧τk Mt∧τk := eg (s)∇u(Xs ) dMsu 0 is a Px -martingale. By definition of the term eg , it is, however, clear that Ex sup|Mt∧τk | < ∞ for all t ≥ 0 and every x ∈ D k∈N which is sufficient for {Mt , t ≥ 0} to be a Px -martingale by the dominated convergence theorem. e. x ∈ D. e. x ∈ D, 0 where we have used the fact that u is essentially bounded by standard elliptic regularity theory.

S. e. x ∈ D into a martingale additive functional of finite energy and a continuous additive functional of zero energy of the non-conservative Hunt process X g associated with (E g , H 1 (D)). We study the relation between the continuous additive functionals N v and N g,v . , v(x) = Gg1 φ(x) = Ex ∞ −t− e t 0 g(Xs ) dLs φ(Xt ) dt 0 for some φ ∈ L2 (D). Then we have the identity −LGg1 φ = φ − v so that for all w ∈ H 1 (D) E g (Gg1 φ, w) = (φ − v)w dx. 29 and the Revuz correspondence, we see that N g,v admits a semimartingale decomposition, namely t Ntg,v = (φ(Xsg ) − v(Xsg )) ds.

12). 26) 0 with t eg (t) := exp − g(Xs ) dLs , t ≥ 0. , the oneparameter family of operators {Ttg , t ≥ 0} defined by Ttg v(x) := Ex eg (t)v(Xt ), x ∈ D and t ≥ 0. 29) where D(E g ) = H 1 (D) by the standard trace theorem. 16. The semigroup {Ttg , t ≥ 0} is associated with the Dirichlet form (E g , D(E g )). Proof. Let Ggα φ, α > 0, denote the Laplace transform of Ttg ∞ Ggα φ(x) = Ex eg (t)e−αt φ(Xt ) dt. 6 it is sufficient to show that Ggα φ ∈ H 1 (D) and Eαg (Ggα φ, v) = φ, v for all φ ∈ L2 (D), v ∈ H 1 (D).

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Anomaly Detection in Random Heterogeneous Media: Feynman-Kac Formulae, Stochastic Homogenization and Statistical Inversion by Martin Simon

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