67,475 research outputs found
A simple proof of Kotake-Narasimhan theorem in some classes of ultradifferentiable functions
No full text[EN] We give a simple proof of a general theorem of Kotake-Narasimhan for elliptic operators in the setting of ultradifferentiable functions in the sense of Braun, Meise and Taylor. We follow the ideas of Komatsu. Based on an example of Metivier, we also show that the ellipticity is a necessary condition for the theorem to be true.C. Boiti and D. Jornet were partially supported by the INdAM-GNAMPA Projects 2014 and 2015.
D. Jornet was partially supported by MINECO, Project MTM2013-43540-PBoiti, C.; Jornet Casanova, D. (2017). A simple proof of Kotake-Narasimhan theorem in some classes of ultradifferentiable functions. Journal of Pseudo-Differential Operators and Applications. 8(2):297-317. https://doi.org/10.1007/s11868-016-0163-yS29731782Boiti, C., Jornet, D.: The problem of iterates in some classes of ultradifferentiable functions. Oper. Theory Adv. Appl. Birkhauser Basel 245, 21–33 (2015)Boiti, C., Jornet, D.: A characterization of the wave front set defined by the iterates of an operator with constant coefficients. arXiv:1412.4954Boiti, C., Jornet, D., Juan-Huguet, J.: Wave front set with respect to the iterates of an operator with constant coefficients. Abstr. Appl. Anal. 2014, 1–17 Article ID 438716 (2014). doi: 10.1155/2014/438716Bolley, P., Camus, J., Mattera, C.: Analyticité microlocale et itérés d’operateurs hypoelliptiques. Séminaire Goulaouic-Schwartz, 1978–1979, Exp No. 13, École Polytech, PalaiseauBonet, J., Meise, R., Melikhov, S.N.: A comparison of two different ways of define classes of ultradifferentiable functions. Bull. Belg. Math. Soc. Simon Stevin 14, 425–444 (2007)Braun, R.W., Meise, R., Taylor, B.A.: Ultradifferentiable functions and Fourier analysis. Result. Math. 17, 206–237 (1990)Fernández, C., Galbis, A.: Superposition in classes of ultradifferentiable functions. Publ. Res. I Math. Sci. 42(2), 399–419 (2006)Jornet Casanova, D.: Operadores Pseudodiferenciales en Clases no Casianalíticas de Tipo Beurling. Universitat Politècnica de València (2004). doi: 10.4995/Thesis/10251/54953Juan-Huguet, J.: Iterates and hypoellipticity of partial differential operators on non-quasianalytic classes. Integr. Equ. Oper. Theory 68, 263–286 (2010)Juan-Huguet, J.: A Paley–Wiener type theorem for generalized non-quasianalytic classes. Stud. Math. 208(1), 31–46 (2012)Komatsu, H.: A characterization of real analytic functions. Proc. Jpn Acad. 36, 90–93 (1960)Komatsu, H.: On interior regularities of the solutions of principally elliptic systems of linear partial differential equations. J. Fac. Sci. Univ. Tokyo Sect. 1, 9, 141–164 (1961)Komatsu, H.: A proof of Kotaké and Narasimhan’s theorem. Proc. Jpn Acad. 38(9), 615–618 (1962)Kotake, T., Narasimhan, M.S.: Regularity theorems for fractional powers of a linear elliptic operator. Bull. Soc. Math. Fr. 90, 449–471 (1962)Kumano-Go, H.: Pseudo-Differential Operators. The MIT Press, Cambridge, London (1982)Langenbruch, M.: P-Funktionale und Randwerte zu hypoelliptischen Differentialoperatoren. Math. Ann. 239(1), 55–74 (1979)Langenbruch, M.: Fortsetzung von Randwerten zu hypoelliptischen Differentialoperatoren und partielle Differentialgleichungen. J. Reine Angew. Math. 311/312, 57–79 (1979)Langenbruch, M.: On the functional dimension of solution spaces of hypoelliptic partial differential operators. Math. Ann. 272, 217–229 (1985)Langenbruch, M.: Bases in solution sheaves of systems of partial differential equations. J. Reine Angew. Math. 373, 1–36 (1987)Lions, J.L., Magenes, E.: Problèmes aux limites non homogènes et applications, vol. 3. Dunod, Paris (1970)Métivier, G.: Propriété des itérés et ellipticité. Commun. Part. Differ. Eq. 3(9), 827–876 (1978)Nelson, E.: Analytic vectors. Ann. Math. 70, 572–615 (1959)Newberger, E., Zielezny, Z.: The growth of hypoelliptic polynomials and Gevrey classes. Proc. Am. Math. Soc. 39(3), 547–552 (1973)Oldrich, J.: Sulla regolarità delle soluzioni delle equazioni lineari ellittiche nelle classi di Beurling. (Italian) Boll. Un. Mat. Ital. (4) 2, 183–195 (1969)Petzsche, H.-J., Vogt, D.: Almost analytic extension of ultradifferentiable functions and the boundary values of holomorphic functions. Math. Ann. 267(1), 17–35 (1984
Abécédaire de l'univers parallèle. À propos de "L'habitació del nen"
No full textComunicació presentada a la 2ème Journée Internationale d'Études: "Le théâtre catalan contemporain. Autour de l'oeuvre de J. M. Benet i Jornet".Comunicació publicada a: "Autour de l'oeuvre du J. M. Benet i Jornet. La chambre de l'enfant. Quatre études et un entretien"Programa Traverses Études catalanes. Organitzat per la Université Paris 8. Traverses. Colegio de España CIUP. París, 27 de gener de 2006
A characterization of the wave front set defined by the iterates of an operator with constant coefficients
No full text[EN] We characterize the wave front set WF*P (u) with respect to the iterates of a linear partial differential operator with constant coefficients of a classical distribution u is an element of D '(Omega), Omega an open subset in R-n. We use recent Paley-Wiener theorems for generalized ultradifferentiable classes in the sense of Braun, Meise and Taylor. We also give several examples and applications to the regularity of operators with variable coefficients and constant strength. Finally, we construct a distribution with prescribed wave front set of this type.The authors were partially supported by FAR2011 (Universita di Ferrara), "Fondi per le necessita di base della ricerca" 2012 and 2013 (Universita di Ferrara) and the INDAM-GNAMPA Project 2014 "Equazioni Differenziali a Derivate Parziali di Evoluzione e Stocastiche" The research of the second author was partially supported by MINECO of Spain, Project MTM2013-43540-P.Boiti, C.; Jornet Casanova, D. (2017). A characterization of the wave front set defined by the iterates of an operator with constant coefficients. Revista de la Real Academia de Ciencias Exactas Físicas y Naturales Serie A Matemáticas. 111(3):891-919. https://doi.org/10.1007/s13398-016-0329-8S8919191113Albanese, A.A., Jornet, D., Oliaro, A.: Quasianalytic wave front sets for solutions of linear partial differential operators. Integr. Equ. Oper. Theory 66, 153–181 (2010)Boiti, C., Jornet, D.: The problem of iterates in some classes of ultradifferentiable functions. In: “Operator Theory: Advances and Applications”. Birkhauser, Basel. 245, 21–32 (2015)Boiti, C., Jornet, D., Juan-Huguet, J.,: Wave front set with respect to the iterates of an operator with constant coefficients. Abstr. Appl. Anal., 1–17 (2014). doi: 10.1155/2014/438716 (Article ID 438716)Bolley, P., Camus, J., Mattera, C.: Analyticité microlocale et itérés d’operateurs hypoelliptiques. In: Séminaire Goulaouic–Schwartz, 1978–79, Exp N.13. École Polytech., PalaiseauBonet, J., Fernández, C., Meise, R.: Characterization of the ω -hypoelliptic convolution operators on ultradistributions. Ann. Acad. Sci. Fenn. Math. 25, 261–284 (2000)Bonet, J., Meise, R., Melikhov, S.N.: A comparison of two different ways to define classes of ultradifferentiable functions. Bull. Belg. Math. Soc. Simon Stevin 14, 425–444 (2007)Braun, R.W., Meise, R., Taylor, B.A.: Ultradifferentiable functions and Fourier analysis. Result. Math. 17, 206–237 (1990)Fernández, C., Galbis, A., Jornet, D.: ω -hypoelliptic differential operators of constant strength. J. Math. Anal. Appl. 297, 561–576 (2004)Fernández, C., Galbis, A., Jornet, D.: Pseudodifferential operators of Beurling type and the wave front set. J. Math. Anal. Appl. 340, 1153–1170 (2008)Hörmander, L.: On interior regularity of the solutions of partial differential equations. Comm. Pure Appl. Math. XI, 197–218 (1958)Hörmander, L.: Uniqueness theorems and wave front sets for solutions of linear partial differential equations with analytic coefficients. Comm. Pure Appl. Math. 24, 671–704 (1971)Hörmander, L.: The Analysis of Linear Partial Differential Operators I. Springer, Berlin (1990)Hörmander, L.: The Analysis of Linear Partial Differential Operators II. Springer, Berlin (1983)Juan-Huguet, J.: Iterates and hypoellipticity of partial differential operators on non-quasianalytic classes. Integr. Equ. Oper. Theory 68, 263–286 (2010)Juan-Huguet, J.: A Paley–Wiener type theorem for generalized non-quasianalytic classes. Studia Math. 208(1), 31–46 (2012)Komatsu, H.: A characterization of real analytic functions. Proc. Jpn. Acad. 36, 90–93 (1960)Kotake, T., Narasimhan, M.S.: Regularity theorems for fractional powers of a linear elliptic operator. Bull. Soc. Math. France 90, 449–471 (1962)Langenbruch, M.: P-Funktionale und Randwerte zu hypoelliptischen Differentialoperatoren. Math. Ann. 239(1), 55–74 (1979)Langenbruch, M.: Fortsetzung von Randwerten zu hypoelliptischen Differentialoperatoren und partielle Differentialgleichungen. J. Reine Angew. Math. 311/312, 57–79 (1979)Langenbruch, M.: On the functional dimension of solution spaces of hypoelliptic partial differential operators. Math. Ann. 272, 217–229 (1985)Langenbruch, M.: Bases in solution sheaves of systems of partial differential equations. J. Reine Angew. Math. 373, 1–36 (1987)Métivier, G.: Propriété des itérés et ellipticité. Comm. Partial Differ. Equ. 3(9), 827–876 (1978)Newberger, E., Zielezny, Z.: The growth of hypoelliptic polynomials and Gevrey classes. Proc. Amer. Math. Soc. 39(3), 547–552 (1973)Rodino, L.: On the problem of the hypoellipticity of the linear partial differential equations. In: Buttazzo, G. (ed.) Developments in Partial Differential Equations and Applications to Mathematical Physics. Plenum Press, New York (1992)Rodino, L.: Linear partial differential operators in Gevrey spaces. World Scientific, Singapore (1993)Zanghirati, L.: Iterates of a class of hypoelliptic operators and generalized Gevrey classes. Boll. U.M.I. Suppl. 1, 177–195 (1980
Improving Kernel Methods for Density Estimation in Random Differential Equations Problems
No full text[EN] Kernel density estimation is a non-parametric method to estimate the probability density function of a random quantity from a finite data sample. The estimator consists of a kernel function and a smoothing parameter called the bandwidth. Despite its undeniable usefulness, the convergence rate may be slow with the number of realizations and the discontinuity and peaked points of the target density may not be correctly captured. In this work, we analyze the applicability of a parametric method based on Monte Carlo simulation for the density estimation of certain random variable transformations. This approach has important applications in the setting of differential equations with input random parameters.This work has been supported by the Spanish Ministerio de Economia, Industria y Competitividad (MINECO), the Agencia Estatal de Investigacion (AEI) and Fondo Europeo de Desarrollo Regional (FEDER UE) grant MTM2017-89664-P.Cortés, J.; Jornet Sanz, M. (2020). Improving Kernel Methods for Density Estimation in Random Differential Equations Problems. Mathematical and Computational Applications (Online). 25(2):1-9. https://doi.org/10.3390/mca25020033S19252Calatayud, J., Cortés, J.-C., Díaz, J. A., & Jornet, M. (2020). Constructing reliable approximations of the probability density function to the random heat PDE via a finite difference scheme. Applied Numerical Mathematics, 151, 413-424. doi:10.1016/j.apnum.2020.01.012Calatayud, J., Cortés, J.-C., & Jornet, M. (2018). The damped pendulum random differential equation: A comprehensive stochastic analysis via the computation of the probability density function. Physica A: Statistical Mechanics and its Applications, 512, 261-279. doi:10.1016/j.physa.2018.08.024Calatayud, J., Cortés, J.-C., Díaz, J. A., & Jornet, M. (2019). Density function of random differential equations via finite difference schemes: a theoretical analysis of a random diffusion-reaction Poisson-type problem. Stochastics, 92(4), 627-641. doi:10.1080/17442508.2019.1645849Calatayud, J., Cortés, J.-C., Dorini, F. A., & Jornet, M. (2020). Extending the study on the linear advection equation subject to stochastic velocity field and initial condition. Mathematics and Computers in Simulation, 172, 159-174. doi:10.1016/j.matcom.2019.12.014Jornet, M., Calatayud, J., Le Maître, O. P., & Cortés, J.-C. (2020). Second order linear differential equations with analytic uncertainties: Stochastic analysis via the computation of the probability density function. Journal of Computational and Applied Mathematics, 374, 112770. doi:10.1016/j.cam.2020.112770Tang, K., Wan, X., & Liao, Q. (2020). Deep density estimation via invertible block-triangular mapping. Theoretical and Applied Mechanics Letters, 10(3), 143-148. doi:10.1016/j.taml.2020.01.023Botev, Z., & Ridder, A. (2017). Variance Reduction. Wiley StatsRef: Statistics Reference Online, 1-6. doi:10.1002/9781118445112.stat0797
Global Wave Front Sets in Ultradifferentiable Classes
No full text[EN] We introduce a global wave front set using Weyl quantizations of pseudodifferential operators of infinite order in the ultradifferentiable setting. We see that in many cases it coincides with the Gabor wave front set already studied by the last three authors of the present work. In this sense, we also extend, to the ultradifferentiable setting, previous work by Rodino and Wahlberg. Finally, we give applications to the study of propagation of singularities of pseudodifferential operators.Funding for open access charge: CRUE-Universitat Politecnica de Valencia.Asensio López, V.; Boiti, C.; Jornet Casanova, D.; Oliaro, A. (2022). Global Wave Front Sets in Ultradifferentiable Classes. Results in Mathematics. 77(2):1-40. https://doi.org/10.1007/s00025-021-01597-xS140772Albanese, A.A., Jornet, D., Oliaro, A.: Quasianalytic wave front sets for solutions of linear partial differential operators. Integral Equ. Oper. Theory 66(2), 153–181 (2010)Albanese, A.A., Jornet, D., Oliaro, A.: Wave front sets for ultradistribution solutions of linear partial differential operators with coefficients in non-quasianalytic classes. Math. Nachr. 285(4), 411–425 (2012)Annovazzi, E.: Esempi notevoli di funzioni gevrey ed ultradistribuzioni. Master’s thesis, University of Torino (1999/2000) ItalianAsensio, V.: Quantizations and global hypoellipticity for pseudodifferential operators of infinite order in classes of ultradifferentiable functions (2022). arXiv:2104.09198. Accepted for publication in Mediterr. J. MathAsensio, V.: Global pseudodifferential operators in spaces of ultradifferentiable functions [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/174847Asensio, V., Jornet, D.: Global pseudodifferential operators of infinite order in classes of ultradifferentiable functions. Rev. R. Acad. Cienc. Exactas Fís. Nat. Ser. A Mat. RACSAM 113(4), 3477–3512 (2019)Björck, G.: Linear partial differential operators and generalized distributions. Ark. Mat. 6, 351–407 (1966)Boiti, C., Jornet, D.: A simple proof of Kotake–Narasimhan theorem in some classes of ultradifferentiable functions. J. Pseudo-Differ. Oper. Appl. 8(2), 297–317 (2017)Boiti, C., Jornet, D.: A characterization of the wave front set defined by the iterates of an operator with constant coefficients. Rev. R. Acad. Cienc. Exactas Fís. Nat. Ser. A Mat. RACSAM 111(3), 891–919 (2017)Boiti, C., Jornet, D., Juan-Huguet, J.: Wave front sets with respect to the iterates of an operator with constant coefficients. Abstr. Appl. Anal. 2014, 438716 (2014)Boiti, C., Jornet, D., Oliaro, A.: Regularity of partial differential operators in ultradifferentiable spaces and Wigner type transforms. J. Math. Anal. Appl. 446(1), 920–944 (2017)Boiti, C., Jornet, D., Oliaro, A.: The Gabor wave front set in spaces of ultradifferentiable functions. Monatsh. Math. 188(2), 199–246 (2019)Boiti, C., Jornet, D., Oliaro, A.: Real Paley–Wiener theorems in spaces of ultradifferentiable functions. J. Funct. Anal. 278(4), 108348 (2020)Boiti, C., Jornet, D., Oliaro, A., Schindl, G.: Nuclearity of rapidly decreasing ultradifferentiable functions and time-frequency analysis. Collect. Math. 72(2), 423–442 (2021)Boiti, C., Jornet, D., Oliaro, A., Schindl, G.: Nuclear global spaces of ultradifferentiable functions in the matrix weighted setting. Banach J. Math. Anal. 15(14), 1–39 (2021)Braun, R.W., Meise, R., Taylor, B.A.: Ultradifferentiable functions and Fourier analysis. Results Math. 17(3–4), 206–237 (1990)Cappiello, M., Schulz, R.: Microlocal analysis of quasianalytic Gelfand–Shilov type ultradistributions. Complex Var. Elliptic Equ. 61(4), 538–561 (2016)Cordero, E., Gröchenig, K.: Time-frequency analysis of localization operators. J. Funct. Anal. 205(1), 107–131 (2003)Debrouwere, A., Neyt, L., Vindas, J.: Characterization of nuclearity for Beurling–Björck spaces. Proc. Am. Math. Soc. 148(12), 5171–5180 (2020)Debrouwere, A., Neyt, L., Vindas, J.: The nuclearity of Gelfand–Shilov spaces and kernel theorems. Collect. Math. 72(1), 203–227 (2021)Fernández, C., Galbis, A.: Superposition in classes of ultradifferentiable functions. Publ. Res. Inst. Math. Sci. 42(2), 399–419 (2006)Fernández, C., Galbis, A., Jornet, D.: -Hypoelliptic differential operators of constant strength. J. Math. Anal. Appl. 297(2), 561–576 (2004). Special issue dedicated to John HorváthFernández, C., Galbis, A., Jornet, D.: Pseudodifferential operators of Beurling type and the wave front set. J. Math. Anal. Appl. 340(2), 1153–1170 (2008)Franken, U.: Weight functions for classes of ultradifferentiable functions. RM 25(1–2), 50–53 (1994)Gröchenig, K.: Foundations of Time-frequency Analysis. Applied and Numerical Harmonic Analysis. 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Computing the density function of complex models with randomness by using polynomial expansions and the RVT technique. Application to the SIR epidemic model
No full text[EN] This paper concerns the computation of the probability density function of the stochastic solution to general complex systems with uncertainties formulated via random differential equations. In the existing literature, the uncertainty quantification for random differential equations is based on the approximation of statistical moments by simulation or spectral methods, or on the computation of the exact density function via the random variable transformation (RVT) method when a closed-form solution is available. However, the problem of approximating the density function in a general setting has not been published yet. In this paper, we propose a hybrid method based on stochastic polynomial expansions, the RVT technique, and multidimensional integration schemes, to obtain accurate approximations to the solution density function. A problem-independent algorithm is proposed. The algorithm is tested on the SIR (susceptible-infected-recovered) epidemiological model, showing significant improvements compared to the previous literature.This work has been supported by the Spanish Ministerio de Economia y Competitividad grant MTM2017-89664-P. The author Marc Jornet acknowledges the doctorate scholarship granted by Programa de Ayudas de Investigacion y Desarrollo (PAID), Universitat Politecnica de Valencia.Calatayud, J.; Cortés, J.; Jornet, M. (2020). Computing the density function of complex models with randomness by using polynomial expansions and the RVT technique. Application to the SIR epidemic model. Chaos, Solitons and Fractals. 133:1-10. https://doi.org/10.1016/j.chaos.2020.109639S110133Strand, J. . (1970). Random ordinary differential equations. Journal of Differential Equations, 7(3), 538-553. doi:10.1016/0022-0396(70)90100-2Crandall, S. H. (1963). Perturbation Techniques for Random Vibration of Nonlinear Systems. The Journal of the Acoustical Society of America, 35(11), 1700-1705. doi:10.1121/1.1918792Villafuerte, L., & Chen-Charpentier, B. M. (2012). A random differential transform method: Theory and applications. Applied Mathematics Letters, 25(10), 1490-1494. doi:10.1016/j.aml.2011.12.033Khan, Y., & Wu, Q. (2011). Homotopy perturbation transform method for nonlinear equations using He’s polynomials. Computers & Mathematics with Applications, 61(8), 1963-1967. doi:10.1016/j.camwa.2010.08.022Khan, Y., Vázquez-Leal, H., & Wu, Q. (2012). An efficient iterated method for mathematical biology model. Neural Computing and Applications, 23(3-4), 677-682. doi:10.1007/s00521-012-0952-zXiu, D., & Karniadakis, G. E. (2002). The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations. SIAM Journal on Scientific Computing, 24(2), 619-644. doi:10.1137/s1064827501387826Chen-Charpentier, B.-M., Cortés, J.-C., Licea, J.-A., Romero, J.-V., Roselló, M.-D., Santonja, F.-J., & Villanueva, R.-J. (2015). Constructing adaptive generalized polynomial chaos method to measure the uncertainty in continuous models: A computational approach. Mathematics and Computers in Simulation, 109, 113-129. doi:10.1016/j.matcom.2014.09.002Cortés, J.-C., Romero, J.-V., Roselló, M.-D., & Villanueva, R.-J. (2017). Improving adaptive generalized polynomial chaos method to solve nonlinear random differential equations by the random variable transformation technique. Communications in Nonlinear Science and Numerical Simulation, 50, 1-15. doi:10.1016/j.cnsns.2017.02.011Ernst, O. G., Mugler, A., Starkloff, H.-J., & Ullmann, E. (2011). On the convergence of generalized polynomial chaos expansions. ESAIM: Mathematical Modelling and Numerical Analysis, 46(2), 317-339. doi:10.1051/m2an/2011045Shi, W., & Zhang, C. (2012). Error analysis of generalized polynomial chaos for nonlinear random ordinary differential equations. Applied Numerical Mathematics, 62(12), 1954-1964. doi:10.1016/j.apnum.2012.08.007Shi, W., & Zhang, C. (2017). Generalized polynomial chaos for nonlinear random delay differential equations. Applied Numerical Mathematics, 115, 16-31. doi:10.1016/j.apnum.2016.12.004Calatayud, J., Cortés, J.-C., & Jornet, M. (2018). On the convergence of adaptive gPC for non-linear random difference equations: Theoretical analysis and some practical recommendations. Journal of Nonlinear Sciences and Applications, 11(09), 1077-1084. doi:10.22436/jnsa.011.09.06Cortés, J.-C., Romero, J.-V., Roselló, M.-D., Santonja, F.-J., & Villanueva, R.-J. (2013). Solving Continuous Models with Dependent Uncertainty: A Computational Approach. Abstract and Applied Analysis, 2013, 1-10. doi:10.1155/2013/983839Calatayud, J., Cortés, J. C., Jornet, M., & Villanueva, R. J. (2018). Computational uncertainty quantification for random time-discrete epidemiological models using adaptive gPC. Mathematical Methods in the Applied Sciences, 41(18), 9618-9627. doi:10.1002/mma.5315Calatayud, J., Cortés, J.-C., & Jornet, M. (2019). Uncertainty quantification for nonlinear difference equations with dependent random inputs via a stochastic Galerkin projection technique. Communications in Nonlinear Science and Numerical Simulation, 72, 108-120. doi:10.1016/j.cnsns.2018.12.011Dorini, F. A., Cecconello, M. S., & Dorini, L. B. (2016). On the logistic equation subject to uncertainties in the environmental carrying capacity and initial population density. Communications in Nonlinear Science and Numerical Simulation, 33, 160-173. doi:10.1016/j.cnsns.2015.09.009Calatayud, J., Cortés, J.-C., & Jornet, M. (2018). The damped pendulum random differential equation: A comprehensive stochastic analysis via the computation of the probability density function. Physica A: Statistical Mechanics and its Applications, 512, 261-279. doi:10.1016/j.physa.2018.08.024Calatayud, J., Cortés, J. C., & Jornet, M. (2018). Uncertainty quantification for random parabolic equations with nonhomogeneous boundary conditions on a bounded domain via the approximation of the probability density function. Mathematical Methods in the Applied Sciences, 42(17), 5649-5667. doi:10.1002/mma.5333Jornet M., Calatayud J., Le Maître O.P., Cortés J.. Second order linear differential equations with analytic uncertainties: stochastic analysis via the computation of the probability density function. 2019. ArXiv:1909.05907.Calatayud, J., Cortés, J.-C., Díaz, J. A., & Jornet, M. (2019). Density function of random differential equations via finite difference schemes: a theoretical analysis of a random diffusion-reaction Poisson-type problem. Stochastics, 92(4), 627-641. doi:10.1080/17442508.2019.1645849Calatayud Gregori, J., Chen-Charpentier, B. M., Cortés López, J. C., & Jornet Sanz, M. (2019). Combining Polynomial Chaos Expansions and the Random Variable Transformation Technique to Approximate the Density Function of Stochastic Problems, Including Some Epidemiological Models. Symmetry, 11(1), 43. doi:10.3390/sym11010043Hethcote, H. W. (2000). The Mathematics of Infectious Diseases. SIAM Review, 42(4), 599-653. doi:10.1137/s0036144500371907Casabán, M.-C., Cortés, J.-C., Romero, J.-V., & Roselló, M.-D. (2015). Probabilistic solution of random SI-type epidemiological models using the Random Variable Transformation technique. Communications in Nonlinear Science and Numerical Simulation, 24(1-3), 86-97. doi:10.1016/j.cnsns.2014.12.016Casabán, M.-C., Cortés, J.-C., Navarro-Quiles, A., Romero, J.-V., Roselló, M.-D., & Villanueva, R.-J. (2016). A comprehensive probabilistic solution of random SIS-type epidemiological models using the random variable transformation technique. 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The Gabor wave front set in spaces of ultradifferentiable functions
No full text[EN] We consider the spaces of ultradifferentiable functions S as introduced by Bjorck (and its dual S) and we use time-frequency analysis to define a suitable wave front set in this setting and obtain several applications: global regularity properties of pseudodifferential operators of infinite order and the micro-pseudolocal behaviour of partial differential operators with polynomial coefficients and of localization operators with symbols of exponential growth. Moreover, we prove that the new wave front set, defined in terms of the Gabor transform, can be described using only Gabor frames. Finally, some examples show the convenience of the use of weight functions to describe more precisely the global regularity of (ultra)distributions.The authors were partially supported by the INdAM-Gnampa Project 2016 "Nuove prospettive nell'analisi microlocale e tempo-frequenza", by FAR2013, FAR2014 (University of Ferrara) and by the project "Ricerca Locale - Analisi di Gabor, operatori pseudodifferenziali ed equazioni differenziali" (University of Torino). The research of the second author was partially supported by the project MTM2016-76647-P.Boiti, C.; Jornet Casanova, D.; Oliaro, A. (2019). The Gabor wave front set in spaces of ultradifferentiable functions. Monatshefte für Mathematik. 188(2):199-246. https://doi.org/10.1007/s00605-018-1242-3S1992461882Albanese, A., Jornet, D., Oliaro, A.: Quasianalytic wave front sets for solutions of linear partial differential operators. Integr. Equ. Oper. Theory 66, 153–181 (2010)Albanese, A., Jornet, D., Oliaro, A.: Wave front sets for ultradistribution solutions of linear partial differential operators with coefficients in non-quasianalytic classes. Math. Nachr. 285(4), 411–425 (2012)Björck, G.: Linear partial differential operators and generalized distributions. Ark. Mat. 6(21), 351–407 (1966)Boiti, C., Gallucci, E.: The overdetermined Cauchy problem for ω -ultradifferentiable functions. Manuscripta Math. 155(3-4), 419–448 (2018)Boiti, C., Jornet, D.: A simple proof of Kotake–Narasimhan theorem in some classes of ultradifferentiable functions. J. Pseudo-Differ. Oper. Appl. 8(2), 297–317 (2017)Boiti, C., Jornet, D.: A characterization of the wave front set defined by the iterates of an operator with constant coefficients. Rev. R. Acad. Cienc. Exactas Fs. Nat. Ser. A Math. RACSAM 111(3), 891–919 (2017)Boiti, C., Jornet, D., Juan-Huguet, J.: Wave front sets with respect to the iterates of an operator with constant coefficients. Abstr. Appl. Anal. 2014, 1–17 (2014). https://doi.org/10.1155/2014/438716Boiti, C., Jornet, D., Oliaro, A.: Regularity of partial differential operators in ultradifferentiable spaces and Wigner type transforms. J. Math. Anal. Appl. 446, 920–944 (2017)Bonet, J., Meise, R., Melikhov, S.N.: A comparison of two different ways to define classes of ultradifferentiable functions. Bull. Belg. Math. Soc. Simon Stevin 14(3), 425–444 (2007)Borwein, J.M., Lewis, A.S.: Convex Analysis and Nonlinear Optimization. Theory and Examples, CMS Books in Mathematics/Ouvrages de Mathématiques de la SMC. Springer, New York (2006)Braun, R.W., Meise, R., Taylor, B.A.: Ultradifferentiable functions and Fourier analysis. Result. Math. 17, 206–237 (1990)Cappiello, M., Schulz, R.: Microlocal analysis of quasianalytic Gelfand–Shilov type ultradistributions. Complex Var. Elliptic Equ. 61(4), 538–561 (2016)Carypis, E., Wahlberg, P.: Propagation of exponential phase space singularities for Schrödinger equations with quadratic Hamiltonians. J. Fourier Anal. Appl. 23(3), 530–571 (2017)Christensen, O.: An Introduction to Frames and Riesz Bases. Applied and Numerical Harmonic Analysis. Springer, Berlin (2016)Fernández, C., Galbis, A., Jornet, D.: Pseudodifferential operators on non-quasianalytic classes of Beurling type. Studia Math. 167(2), 99–131 (2005)Fernández, C., Galbis, A., Jornet, D.: Pseudodifferential operators of Beurling type and the wave front set. J. Math. Anal. Appl. 340(2), 1153–1170 (2008)Fieker, C.: P -Konvexität und ω -Hypoelliptizität für partielle Differentialoperatoren mit konstanten Koeffizienten. Diplomarbeit, Mathematischen Institut der Heinrich-Heine-Universität Düsseldorf (1993)Gröchenig, K.: Foundations of Time-Frequency Analysis. Birkhäuser, Boston (2001)Gröchenig, K., Zimmermann, G.: Spaces of test functions via the STFT. J. Funct. Spaces Appl. 2(1), 25–53 (2004)Heil, C.: A Basis Theory Primer. Applied and Numerical Harmonic Analysis. Springer, New York (2011)Hörmander, L.: Fourier integral operators. Acta Math. 127(1), 79–183 (1971)Hörmander, L.: Quadratic hyperbolic operators. In: Cattabriga, L., Rodino, L. (eds.) Microlocal Analysis and Applications. Lecture Notes in Mathematics, pp. 118–160. Springer, Berlin (1991)Hörmander, L.: The Analysis of Linear Partial Differential Operators, vol. I. Springer-Verlag, Berlin (1983)Hörmander, L.: The Analysis of Linear Partial Differential Operators, vol. II. Springer-Verlag, Berlin (1983)Hörmander, L.: The Analysis of Linear Partial Differential Operators, vol. III. Springer-Verlag, Berlin (1985)Janssen, A.J.E.M.: Duality and biorthogonality for Weyl–Heisenberg frames. J. Fourier Anal. Appl. 1(4), 403–436 (1995)Langenbruch, M.: Hermite functions and weighted spaces of generalized functions. Manuscripta Math. 119(3), 269–285 (2006)Meise, R., Vogt, D.: Introduction to Functional Analysis. Oxford Science Publications, Clarendon Press, Oxford (1997)Nakamura, S.: Propagation of the homogeneous wave front set for Schrödinger equations. Duke Math. J. 126, 349–367 (2005)Nicola, F., Rodino, L.: Global Pseudo-Differential Calculus on Euclidean Spaces. Springer, Basel (2010)Pilipović, S.: Tempered ultradistributions. Boll. U.M.I. B (7) 2(2), 235-251 (1988)Prangoski, B.: Pseudodifferential operators of infinite order in spaces of tempered ultradistributions. J. Pseudo-Differ. Oper. Appl. 4(4), 495–549 (2013)Pilipović, S., Prangoski, B.: Anti-Wick and Weyl quantization on ultradistribution spaces. J. Math. Pures Appl. 103(2), 472–503 (2015)Rodino, L.: Linear Partial Differential Operators and Gevrey Spaces. World Scientific Publishing Co., Inc., River Edge, NJ (1993)Rodino, L., Wahlberg, P.: The Gabor wave front set. Monatsh. 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Focused beam routing protocol for underwater acoustic networks
No full textMulti-hop transmission is considered for large coverage areas in bandwidth-limited underwater acoustic networks. In this paper, we present a scalable routing technique based on location information, and optimized for minimum energy per bit consumption. The proposed Focused Beam Routing (FBR) protocol is suitable for networks containing both static and mobile nodes, which are not necessarily synchronized to a global clock. A source node must be aware of its own location and the location of its final destination, but not those of other nodes.
The FBR protocol can be defined as a cross-layer approach, in which the routing protocol, the medium access control and the physical layer functionalities are tightly coupled by power control. It can be described as a distributed algorithm, in which a route is dynamically established as the data packet traverses the network towards its final destination. The selection of the next relay is made at each step of the path after suitable candidates have proposed themselves.
The system performance is measured in terms of energy per bit consumption and average packet end-to-end delay. The results are compared to those obtained using pre-established routes, defined via Dijkstra's algorithm for minimal power consumption. It is shown that the protocol's performance is close to the ideal case, as the additional burden of dynamic route discovery is minimal
Analytic solution to the generalized delay diffusion equation with uncertain inputs in the random Lebesgue sense
No full text[EN] In this paper, we deal with the randomized generalized diffusion equation with delay:u(t)(t, x) = a(2)u(xx)(t, x) + b(2)u(xx)(t - tau, x),t > tau,0 = 0;u(t,x)=phi(t,x),0 0andl > 0are constant. The coefficientsa(2)andb(2)are nonnegative random variables, and the initial condition phi(t, x)and the solutionu(t, x)are random fields. The separation of variables method develops a formal series solution. We prove that the series satisfies the delay diffusion problem in the random Lebesgue sense rigorously. By truncating the series, the expectation and the variance of the random-field solution can be approximated.Secretaria de Estado de Investigacion, Desarrollo e Innovacion, Grant/Award Number: MTM2017-89664-P; Spanish Ministerio de Economia, Industria y Competitividad (MINECO); Agencia Estatal de Investigacion (AEI); Fondo Europeo de Desarrollo Regional (FEDER UE), Grant/Award Number: MTM2017-89664-PCortés, J.; Jornet, M. (2021). Analytic solution to the generalized delay diffusion equation with uncertain inputs in the random Lebesgue sense. Mathematical Methods in the Applied Sciences. 44(2):2265-2272. https://doi.org/10.1002/mma.6921S22652272442Smith, H. (2011). An Introduction to Delay Differential Equations with Applications to the Life Sciences. Texts in Applied Mathematics. doi:10.1007/978-1-4419-7646-8Driver, R. D. (1977). Ordinary and Delay Differential Equations. Applied Mathematical Sciences. doi:10.1007/978-1-4684-9467-9Kolmanovskii, V., & Myshkis, A. (1999). Introduction to the Theory and Applications of Functional Differential Equations. doi:10.1007/978-94-017-1965-0Wu, J. (1996). Theory and Applications of Partial Functional Differential Equations. Applied Mathematical Sciences. doi:10.1007/978-1-4612-4050-1Diekmann, O., Verduyn Lunel, S. M., van Gils, S. A., & Walther, H.-O. (1995). Delay Equations. Applied Mathematical Sciences. doi:10.1007/978-1-4612-4206-2Hale, J. K. (1977). 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Journal of Computational and Applied Mathematics, 291, 76-84. doi:10.1016/j.cam.2014.11.025ErneuxT.Applied Delay Differential Equations Surveys and Tutorials in the Applied Mathematical Sciences Series:Springer New York;2009.Kyrychko, Y. N., & Hogan, S. J. (2010). On the Use of Delay Equations in Engineering Applications. Journal of Vibration and Control, 16(7-8), 943-960. doi:10.1177/1077546309341100Matsumoto, A., & Szidarovszky, F. (2009). Delay Differential Nonlinear Economic Models. Nonlinear Dynamics in Economics, Finance and Social Sciences, 195-214. doi:10.1007/978-3-642-04023-8_11Harding, L., & Neamţu, M. (2016). A Dynamic Model of Unemployment with Migration and Delayed Policy Intervention. Computational Economics, 51(3), 427-462. doi:10.1007/s10614-016-9610-3Xiu, D. (2010). Numerical Methods for Stochastic Computations. doi:10.2307/j.ctv7h0skvLe Maître, O. P., & Knio, O. M. (2010). Spectral Methods for Uncertainty Quantification. Scientific Computation. doi:10.1007/978-90-481-3520-2NeckelT RuppF.Random Differential Equations in Scientific Computing. Walter de Gruyter;2013.Villafuerte, L., Braumann, C. A., Cortés, J.-C., & Jódar, L. (2010). Random differential operational calculus: Theory and applications. Computers & Mathematics with Applications, 59(1), 115-125. doi:10.1016/j.camwa.2009.08.061Calatayud, J., Cortés, J.-C., & Jornet, M. (2019). Improving the Approximation of the First- and Second-Order Statistics of the Response Stochastic Process to the Random Legendre Differential Equation. Mediterranean Journal of Mathematics, 16(3). doi:10.1007/s00009-019-1338-6Licea, J. A., Villafuerte, L., & Chen-Charpentier, B. M. (2013). Analytic and numerical solutions of a Riccati differential equation with random coefficients. Journal of Computational and Applied Mathematics, 239, 208-219. doi:10.1016/j.cam.2012.09.040Burgos, C., Calatayud, J., Cortés, J.-C., & Villafuerte, L. (2018). Solving a class of random non-autonomous linear fractional differential equations by means of a generalized mean square convergent power series. Applied Mathematics Letters, 78, 95-104. doi:10.1016/j.aml.2017.11.009Calatayud, J., Cortés, J.-C., & Jornet, M. (2019). Random differential equations with discrete delay. Stochastic Analysis and Applications, 37(5), 699-707. doi:10.1080/07362994.2019.1608833Calatayud, J., Cortés, J.-C., & Jornet, M. (2019). -calculus Approach to the Random Autonomous Linear Differential Equation with Discrete Delay. Mediterranean Journal of Mathematics, 16(4). doi:10.1007/s00009-019-1370-6Martín, J. A., Rodríguez, F., & Company, R. (2004). Analytic solution of mixed problems for thegeneralized diffusion equation with delay. Mathematical and Computer Modelling, 40(3-4), 361-369. doi:10.1016/j.mcm.2003.10.046Martínez-Cervantes, G. (2016). Riemann integrability versus weak continuity. 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Global pseudodifferential operators of infinite order in classes of ultradifferentiable functions
No full text[EN] We develop a theory of pseudodifferential operators of infinite order for the global classes S. of ultradifferentiable functions in the sense of Bjorck, following the previous ideas given by Prangoski for ultradifferentiable classes in the sense of Komatsu. We study the composition and the transpose of such operators with symbolic calculus and provide several examples.The first author was partially supported by the project GV Prometeo 2017/102, and the second author by the project MTM2016-76647-P. This article is part of the PhD. Thesis of V. Asensio. The authors are very grateful to the two referees for the careful reading and their suggestions and comments, which improved the paper.Asensio, V.; Jornet Casanova, D. (2019). Global pseudodifferential operators of infinite order in classes of ultradifferentiable functions. Revista de la Real Academia de Ciencias Exactas Físicas y Naturales Serie A Matemáticas. 113(4):3477-3512. https://doi.org/10.1007/s13398-019-00710-8S347735121134Albanese, A.A., Jornet, D., Oliaro, A.: Wave front sets for ultradistribution solutions of linear partial differential operators with coefficients in non-quasianalytic classes. Math. Nachr. 285(4), 411–425 (2012)Björck, G.: Linear partial differential operators and generalized distributions. Ark. Mat. 6, 351–407 (1966)Boiti, C., Jornet, D., Oliaro, A.: Regularity of partial differential operators in ultradifferentiable spaces and Wigner type transforms. J. Math. Anal. Appl. 446(1), 920–944 (2017)Bonet, J., Meise, R., Melikhov, S.N.: A comparison of two different ways to define classes of ultradifferentiable functions. Bull. Belg. Math. Soc. Simon Stevin 14(3), 425–444 (2007)Braun, R.W., Meise, R., Taylor, B.A.: Ultradifferentiable functions and Fourier analysis. Results Math. 17(3–4), 206–237 (1990)Braun, R.W.: An extension of Komatsu’s second structure theorem for ultradistributions. J. Fac. Sci. Univ. Tokyo Sect. IA Math. 40(2), 411–417 (1993)Cappiello, M.: Fourier integral operators of infinite order and applications to SG-hyperbolic equations. Tsukuba J. Math. 28(2), 311–361 (2004)Cappiello, M., Pilipović, S., Prangoski, B.: Parametrices and hypoellipticity for pseudodifferential operators on spaces of tempered ultradistributions. J. Pseudo-Differ. Oper. Appl. 5(4), 491–506 (2014)Fernández, C., Galbis, A., Jornet, D.: -hypoelliptic differential operators of constant strength. J. Math. Anal. Appl. 297(2), 561–576 (2004). Special issue dedicated to John HorváthFernández, C., Galbis, A., Jornet, D.: Pseudodifferential operators on non-quasianalytic classes of Beurling type. Studia Math. 167(2), 99–131 (2005)Fernández, C., Galbis, A., Jornet, D.: Pseudodifferential operators of Beurling type and the wave front set. J. Math. Anal. Appl. 340(2), 1153–1170 (2008)Hashimoto, S., Morimoto, Y., Matsuzawa, T.: Opérateurs pseudodifférentiels et classes de Gevrey. Commun. Partial Differ. Equ. 8(12), 1277–1289 (1983)Hörmander, L.: Pseudo-differential operators. Commun. Pure Appl. Math. 18, 501–517 (1965)Kohn, J.J., Nirenberg, L.: An algebra of pseudo-differential operators. Commun. Pure Appl. Math. 18, 269–305 (1965)Komatsu, H.: Ultradistributions. I. Structure theorems and a characterization. J. Fac. Sci. Univ. Tokyo Sect. IA Math. 20, 25–105 (1973)Langenbruch, M.: Continuation of Gevrey regularity for solutions of partial differential operators. In Functional analysis (Trier, 1994), pages 249–280. de Gruyter, Berlin (1996)Nicola, F.: Rodino, Luigi: Global pseudo-differential calculus on Euclidean spaces, volume 4 of Pseudo-Differential Operators. Theory and Applications. Birkhäuser Verlag, Basel (2010)Prangoski, B.: Pseudodifferential operators of infinite order in spaces of tempered ultradistributions. J. Pseudo-Differ. Oper. Appl. 4(4), 495–549 (2013)Rodino, L.: Linear partial differential operators in Gevrey spaces. World Scientific Publishing Co., Inc., River Edge (1993)Shubin, M.A.: Pseudodifferential operators and spectral theory. Springer-Verlag, Berlin, second edition. Translated from the 1978 Russian original by Stig I. Andersson (2001)Zanghirati, L.: Pseudodifferential operators of infinite order and Gevrey classes. Ann. Univ. Ferrara Sez. VII (N.S.) 31, 197–219, 1985 (1986
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