Model order reduction for (stochastic-) delay equations with error bounds

Computational Method for Fractional-Order Stochastic Delay Differential Equations

Dynamic systems in many branches of science and industry are often perturbed by various types of environmental noise. Analysis of this class of models are very popular among researchers. In this paper, we present a method for approximating solution of fractional-order stochastic delay differential equations driven by Brownian motion. The fractional derivatives are considered in the Caputo sense...

Establishing Global Error Bounds for Model Reduction in

In addition to theory and experiment, simulation of reacting flows has become important in policymaking, industry, and combustion science. However, simulations of reacting flows can be extremely computationally demanding due to the wide range of length scales involved in turbulence, the wide range of time scales involved in chemical reactions, and the large number of species in detailed chemica...

Establishing Global Error Bounds for Model Reduction in Combustion

In addition to theory and experiment, simulation of reacting flows has become important in policymaking, industry, and combustion science. However, simulations of reacting flows can be extremely computationally demanding due to the wide range of length scales involved in turbulence, the wide range of time scales involved in chemical reactions, and the large number of species in detailed chemica...

Stochastic Subspace Identification: Valid Model, Asymptotics and Model Error Bounds

This paper investigates the ability of the stochastic subspace identification technique to return a valid model from finite measurement data, its asymptotic properties as the data set becomes large, and asymptotic error bounds of the identified model (in terms of H2 and H∞ norms). First, a new and straightforward LMI-based approach is proposed, which returns a valid identified model even in cas...

Regularity of stochastic delay equations under pth order degeneracy