Concept

Stochastic calculus

Related publications (165)

Stabilized Numerical Methods for Stochastic Differential Equations driven by Diffusion and Jump-Diffusion Processes

Stochastic models that account for sudden, unforeseeable events play a crucial role in many different fields such as finance, economics, biology, chemistry, physics and so on. That kind of stochastic problems can be modeled by stochastic differential equat ...

EPFL2015

Analysis and computation of the elastic wave equation with random coefficients

Fabio Nobile

We analyze the stochastic initial-boundary value problem for the elastic wave equation with random coefficients and deterministic data. We propose a stochastic collocation method for computing statistical moments of the solution or statistics of some given ...

2015

Compressibility of Symmetric-α-Stable Processes

Michaël Unser, Julien René Pierre Fageot, John Paul Ward

Within a deterministic framework, it is well known that n-term wavelet approximation rates of functions can be deduced from their Besov regularity. We use this principle to determine approximation rates for symmetric-α-stable (SαS) stochastic processes. Fi ...

IEEE2015

Phase derivative estimation from a single interferogram using a Kalman smoothing algorithm

Pramod Rastogi, Rishikesh Dilip Kulkarni

We report a technique for direct phase derivative estimation from a single recording of a complex interferogram. In this technique, the interference field is represented as an autoregressive model with spatially varying coefficients. Estimates of these coe ...

Optical Society of America2015

Upper bounds for the reach-avoid probability via robust optimization

Maryam Kamgarpour, John Lygeros, Tyler Summers

We consider finite horizon reach-avoid problems for discrete time stochastic systems. Our goal is to construct upper bound functions for the reach-avoid probability by means of tractable convex optimization problems. We achieve this by restricting attentio ...

2015

Low-rank tensor approximation for high-order correlation functions of Gaussian random fields

Fabio Nobile, Daniel Kressner, Christine Tobler, Rajesh Kumar

Gaussian random fields are widely used as building blocks for modeling stochastic processes. This paper is concerned with the efficient representation of d-point correlations for such fields, which in turn enables the representation of more general stochas ...

2015

Sampling and (Sparse) Stochastic Processes: A Tale of Splines and Innovation

Michaël Unser

The commonality between splines and Gaussian or sparse stochastic processes is that they are ruled by the same type of differential equations. Our purpose here is to demonstrate that this has profound implications for the three primary forms of sampling: u ...

IEEE2015

Sensitivity and open-loop control of stochastic response in a noise amplifier flow: the backward-facing step

François Gallaire, Edouard Boujo

The two-dimensional backward-facing step flow is a canonical example of noise amplifier flow: global linear stability analysis predicts that it is stable, but perturbations can undergo large amplification in space and time as a result of non-normal effects ...

Cambridge University Press2015

Microstructural influence on the scatter in the fatigue life of steel reinforcement bars

Alain Nussbaumer, Marina Rocha Pinto Portela Nunes

Fatigue damage accumulation and failure of steel reinforcement bars (rebars) is a stochastic process. Scatter can be influenced by the sensitivity of the short crack growth to the microstructural features, especially near the fatigue limit. This work inves ...

Elsevier2015

Automatic Identification and Calibration of Stochastic Parameters in Inertial Sensors

Jan Skaloud, Stéphane Guerrier

We present an algorithm for determining the nature of stochastic processes and their parameters based on the analysis of time series of inertial errors. The algorithm is suitable mainly (but not only) for situations where several stochastic processes are s ...

Wiley Periodicals, Inc2015

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