Conditional expectations associated with quantum states

An extension of the conditional expectations (those under a given subalgebra of events and not the simple ones under a single event) from the classical to the quantum case is presented. In the classical case, the conditional expectations always exist; in the quantum case, however, they exist only if a certain weak compatibility criterion is satisfied. This compatibility criterion was introduced among others in a recent paper by the author. Then, state-independent conditional expectations and quantum Markov processes are studied. A classical Markov process is a probability measure, together with a system of random variables, satisfying the Markov property and can equivalently be described by a system of Markovian kernels (often forming a semigroup). This equivalence is partly extended to quantum probabilities. It is shown that a dynamical (semi)group can be derived from a given system of quantum observables satisfying the Markov property, and the group generators are studied. The results are presented in the framework of Jordan operator algebras, and a very general type of observables (including the usual real-valued observables or self-adjoint operators) is considered.Comment: 10 pages, the original publication is available at http://www.aip.or

Detection and Characterization of Exoplanets and Disks using Projections on Karhunen-Loeve Eigenimages

We describe a new method to achieve point spread function (PSF) subtractions for high- contrast imaging using Principal Component Analysis (PCA) that is applicable to both point sources or extended objects (disks). Assuming a library of reference PSFs, a Karhunen-Lo`eve transform of theses references is used to create an orthogonal basis of eigenimages, on which the science target is projected. For detection this approach provides comparable suppression to the Locally Optimized Combination of Images (LOCI) algorithm, albeit with increased robustness to the algorithm parameters and speed enhancement. For characterization of detected sources the method enables forward modeling of astrophysical sources. This alleviates the biases in the astrometry and photometry of discovered faint sources, which are usually associated with LOCI- based PSF subtractions schemes. We illustrate the algorithm performance using archival Hubble Space Telescope (HST) images, but the approach may also be considered for ground-based data acquired with Angular Differential Imaging (ADI) or integral-field spectrographs (IFS).Comment: 12 pages, 4 figure

Determining the Spectral Signature of Spatial Coherent Structures

We applied to an open flow a proper orthogonal decomposition (pod) technique, on 2D snapshots of the instantaneous velocity field, to reveal the spatial coherent structures responsible of the self-sustained oscillations observed in the spectral distribution of time series. We applied the technique to 2D planes out of 3D direct numerical simulations on an open cavity flow. The process can easily be implemented on usual personal computers, and might bring deep insights on the relation between spatial events and temporal signature in (both numerical or experimental) open flows.Comment: 4 page

Principal manifolds and graphs in practice: from molecular biology to dynamical systems

We present several applications of non-linear data modeling, using principal manifolds and principal graphs constructed using the metaphor of elasticity (elastic principal graph approach). These approaches are generalizations of the Kohonen's self-organizing maps, a class of artificial neural networks. On several examples we show advantages of using non-linear objects for data approximation in comparison to the linear ones. We propose four numerical criteria for comparing linear and non-linear mappings of datasets into the spaces of lower dimension. The examples are taken from comparative political science, from analysis of high-throughput data in molecular biology, from analysis of dynamical systems.Comment: 12 pages, 9 figure

Almost sure exponential stability of backward Euler–Maruyama discretizations for hybrid stochastic differential equations

This is a continuation of the first author's earlier paper [1] jointly with Pang and Deng, in which the authors established some sufficient conditions under which the Euler-Maruyama (EM) method can reproduce the almost sure exponential stability of the test hybrid SDEs. The key condition imposed in [1] is the global Lipschitz condition. However, we will show in this paper that without this global Lipschitz condition the EM method may not preserve the almost sure exponential stability. We will then show that the backward EM method can capture the almost sure exponential stability for a certain class of highly nonlinear hybrid SDEs

Gaussian limits for discrepancies. I: Asymptotic results

We consider the problem of finding, for a given quadratic measure of non-uniformity of a set of $N$ points (such as $L_2$ star-discrepancy or diaphony), the asymptotic distribution of this discrepancy for truly random points in the limit $N\to\infty$. We then examine the circumstances under which this distribution approaches a normal distribution. For large classes of non-uniformity measures, a Law of Many Modes in the spirit of the Central Limit Theorem can be derived.Comment: 25 pages, Latex, uses fleqn.sty, a4wide.sty, amsmath.st

Mean square convergent non-standard numerical schemes for linear random differential equations with delay

[EN] In this paper, we are concerned with the construction of numerical schemes for linear random differential equations with discrete delay. For the linear deterministic differential equation with discrete delay, a recent contribution proposed a family of non-standard finite difference (NSFD) methods from an exact numerical scheme on the whole domain. The family of NSFD schemes had increasing order of accuracy, was dynamically consistent, and possessed simple computational properties compared to the exact scheme. In the random setting, when the two equation coefficients are bounded random variables and the initial condition is a regular stochastic process, we prove that the randomized NSFD schemes converge in the mean square (m.s.) sense. M.s. convergence allows for approximating the expectation and the variance of the solution stochastic process. In practice, the NSFD scheme is applied with symbolic inputs, and afterward the statistics are explicitly computed by using the linearity of the expectation. This procedure permits retaining the increasing order of accuracy of the deterministic counterpart. Some numerical examples illustrate the approach. The theoretical m.s. convergence rate is supported numerically, even when the two equation coefficients are unbounded random variables. M.s. dynamic consistency is assessed numerically. A comparison with Euler's method is performed. Finally, an example dealing with the time evolution of a photosynthetic bacterial population is presented.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.Calatayud, J.; Cortés, J.; Jornet, M.; Rodríguez, F. (2020). Mean square convergent non-standard numerical schemes for linear random differential equations with delay. Mathematics. 8(9):1-17. https://doi.org/10.3390/math8091417S11789Bocharov, G. A., & Rihan, F. A. (2000). Numerical modelling in biosciences using delay differential equations. Journal of Computational and Applied Mathematics, 125(1-2), 183-199. doi:10.1016/s0377-0427(00)00468-4Jackson, M., & Chen-Charpentier, B. M. (2017). Modeling plant virus propagation with delays. Journal of Computational and Applied Mathematics, 309, 611-621. doi:10.1016/j.cam.2016.04.024Chen-Charpentier, B. M., & Diakite, I. (2016). A mathematical model of bone remodeling with delays. Journal of Computational and Applied Mathematics, 291, 76-84. doi:10.1016/j.cam.2014.11.025Kyrychko, 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:10.1177/1077546309341100Harding, L., & Neamţu, M. (2016). A Dynamic Model of Unemployment with Migration and Delayed Policy Intervention. Computational Economics, 51(3), 427-462. doi:10.1007/s10614-016-9610-3Mickens, R. E. (2005). Dynamic consistency: a fundamental principle for constructing nonstandard finite difference schemes for differential equations. Journal of Difference Equations and Applications, 11(7), 645-653. doi:10.1080/10236190412331334527Patidar, K. C. (2016). Nonstandard finite difference methods: recent trends and further developments. Journal of Difference Equations and Applications, 22(6), 817-849. doi:10.1080/10236198.2016.1144748García, M. A., Castro, M. A., Martín, J. A., & Rodríguez, F. (2018). Exact and nonstandard numerical schemes for linear delay differential models. Applied Mathematics and Computation, 338, 337-345. doi:10.1016/j.amc.2018.06.029Castro, M. Á., García, M. A., Martín, J. A., & Rodríguez, F. (2019). Exact and Nonstandard Finite Difference Schemes for Coupled Linear Delay Differential Systems. Mathematics, 7(11), 1038. doi:10.3390/math7111038Villafuerte, 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.2009.08.061Cortés, J.-C., Jódar, L., Roselló, M.-D., & Villafuerte, L. (2012). Solving initial and two-point boundary value linear random differential equations: A mean square approach. Applied Mathematics and Computation, 219(4), 2204-2211. doi:10.1016/j.amc.2012.08.066Calatayud, J., Cortés, J.-C., Jornet, M., & Villafuerte, L. (2018). Random non-autonomous second order linear differential equations: mean square analytic solutions and their statistical properties. Advances in Difference Equations, 2018(1). doi:10.1186/s13662-018-1848-8Calatayud, 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). $$\mathrm {L}^p$$-calculus Approach to the Random Autonomous Linear Differential Equation with Discrete Delay. Mediterranean Journal of Mathematics, 16(4). doi:10.1007/s00009-019-1370-6Cortés, J. C., & Jornet, M. (2020). Lp-Solution to the Random Linear Delay Differential Equation with a Stochastic Forcing Term. Mathematics, 8(6), 1013. doi:10.3390/math8061013Caraballo, T., Cortés, J.-C., & Navarro-Quiles, A. (2019). Applying the random variable transformation method to solve a class of random linear differential equation with discrete delay. Applied Mathematics and Computation, 356, 198-218. doi:10.1016/j.amc.2019.03.048Cortés, J. C., Jódar, L., & Villafuerte, L. (2007). Numerical solution of random differential equations: A mean square approach. Mathematical and Computer Modelling, 45(7-8), 757-765. doi:10.1016/j.mcm.2006.07.017Cortés, J. C., Jódar, L., & Villafuerte, L. (2007). Mean square numerical solution of random differential equations: Facts and possibilities. 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An extension of Wiener integration with the use of operator theory

With the use of tensor product of Hilbert space, and a diagonalization procedure from operator theory, we derive an approximation formula for a general class of stochastic integrals. Further we establish a generalized Fourier expansion for these stochastic integrals. In our extension, we circumvent some of the limitations of the more widely used stochastic integral due to Wiener and Ito, i.e., stochastic integration with respect to Brownian motion. Finally we discuss the connection between the two approaches, as well as a priori estimates and applications.Comment: 13 page

A weighted reduced basis method for parabolic PDEs with random data

This work considers a weighted POD-greedy method to estimate statistical outputs parabolic PDE problems with parametrized random data. The key idea of weighted reduced basis methods is to weight the parameter-dependent error estimate according to a probability measure in the set-up of the reduced space. The error of stochastic finite element solutions is usually measured in a root mean square sense regarding their dependence on the stochastic input parameters. An orthogonal projection of a snapshot set onto a corresponding POD basis defines an optimum reduced approximation in terms of a Monte Carlo discretization of the root mean square error. The errors of a weighted POD-greedy Galerkin solution are compared against an orthogonal projection of the underlying snapshots onto a POD basis for a numerical example involving thermal conduction. In particular, it is assessed whether a weighted POD-greedy solutions is able to come significantly closer to the optimum than a non-weighted equivalent. Additionally, the performance of a weighted POD-greedy Galerkin solution is considered with respect to the mean absolute error of an adjoint-corrected functional of the reduced solution.Comment: 15 pages, 4 figure