0000000000336739
AUTHOR
Ana Navarro-quiles
Introducing randomness in the analysis of chemical reactions: An analysis based on random differential equations and probability density functions
[EN] In this work we consider a particular randomized kinetic model for reaction-deactivation of hydrogen peroxide decomposition. We apply the Random Variable Transformation technique to obtain the first probability density function of the solution stochastic process under general conditions. From the rst probability density function, we can obtain fundamental statistical information, such as the mean and the variance of the solution, at every instant time. The transformation considered in the application of the Random Variable Transformation technique is not unique. Then, the first probability density function can take different expressions, although essentially equivalent in terms of comp…
First-order linear differential equations whose data are complex random variables: Probabilistic solution and stability analysis via densities
[EN] Random initial value problems to non-homogeneous first-order linear differential equations with complex coefficients are probabilistically solved by computing the first probability density of the solution. For the sake of generality, coefficients and initial condition are assumed to be absolutely continuous complex random variables with an arbitrary joint probability density function. The probability of stability, as well as the density of the equilibrium point, are explicitly determined. The Random Variable Transformation technique is extensively utilized to conduct the overall analysis. Several examples are included to illustrate all the theoretical findings.
Effect of uncertain damping coefficient on the response of a SDOF system
In this paper, a full probabilistic description of the response of a randomized SDOF system in both the time and the frequency domain is done. Considering that the damping of the structure does not simply relate to any single physical phenomenon, the sensitivity of the response to the randomness of the damping parameter is investigated. The stochastic analysis is conducted via the Probability Transformation Method therefore the first probability density function of the response is evaluated. The effect of the uncertain damping coefficient on the response of the SDOF system has been investigated through several numerical examples. From the response probability density function as well as fro…
Solving fully randomized higher-order linear control differential equations: Application to study the dynamics of an oscillator
[EN] In this work, we consider control problems represented by a linear differential equation assuming that all the coefficients are random variables and with an additive control that is a stochastic process. Specifically, we will work with controllable problems in which the initial condition and the final target are random variables. The probability density function of the solution and the control has been calculated. The theoretical results have been applied to study, from a probabilistic standpoint, a damped oscillator.
Statistical Analysis of Biological Models with Uncertainty
In this contribution relevant biological models, based on random differential equations, are studied. For the sake of generality, we assume that the initial condition and the biological model parameters are dependent random variables with arbitrary probability distributions. We present a general methodology that enables us to provide a full probabilistic description of the solution stochastic process for each stochastic model. The statistical analysis is performed through the calculation of the first probability function by applying the random variable transformation technique. From the first probability density function, we can calculate any one-dimensional moment of the solution, includin…
Solving fully randomized first-order linear control systems: Application to study the dynamics of a damped oscillator with parametric noise under stochastic control
[EN] This paper is devoted to study random linear control systems where the initial condition, the final target, and the elements of matrices defining the coefficients are random variables, while the control is a stochastic process. The so-called Random Variable Transformation technique is adapted to obtain closed-form expressions of the probability density functions of the solution and of the control. The theoretical findings are applied to study the dynamics of a damped oscillator subject to parametric noise.