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Creators/Authors contains: "Tindel, Samy"

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  1. ; ; ; (, Stochastic Processes and their Applications)
    Free, publicly-accessible full text available September 1, 2026
  2. ; ; ; (, Stochastics and Partial Differential Equations: Analysis and Computations)
    Free, publicly-accessible full text available May 25, 2026
  3. ; ; (, Stochastic Processes and their Applications)
    Full Text Available 
  4. ; (, Stochastic Processes and their Applications)
    Full Text Available 
  5. ; ; ; (, Journal of Functional Analysis)
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  6. ; (, Asymptotic Analysis)
    We study the effect of additive noise to the inversion of FIOs associated to a diffeomorphic canonical relation. We use the microlocal defect measures to measure the power spectrum of the noise in the phase space and analyze how that power spectrum is transformed under the inversion. In general, white noise, for example, is mapped to noise depending on the position and on the direction. In particular, we compute the standard deviation, locally, of the noise added to the inversion as a function of the standard deviation of the noise added to the data. As an example, we study the Radon transform in the plane in parallel and fan-beam coordinates, and present numerical examples. 
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  7. ; ; ; (, International Mathematics Research Notices)
    Abstract We study a wave equation in dimension $$d\in \{1,2\}$$ with a multiplicative space-time Gaussian noise. The existence and uniqueness of the Stratonovich solution is obtained under some conditions imposed on the Gaussian noise. The strategy is to develop some Strichartz-type estimates for the wave kernel in weighted Besov spaces, by which we can prove the well-posedness of an associated Young-type equation. Those Strichartz bounds are of independent interest. 
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  8. ; ; (, Journal of theoretical probability)
    Accepted Manuscript Full Text Available
  9. ; ; (, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences)
    We introduce a proper notion of two-dimensional signature for images. This object is inspired by the so-called rough paths theory, and it captures many essential features of a two-dimensional object such as an image. It thus serves as a low-dimensional feature for pattern classification. Here, we implement a simple procedure for texture classification. In this context, we show that a low-dimensional set of features based on signatures produces an excellent accuracy. 
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  10. ; ; ; (, Queueing Systems)
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