## About

24

Publications

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Introduction

I am currently working on uncertainty quantification for complex network systems, with a focus on natural gas networks.
First, I work on efficient numerical methods for gas networks modelling and simulation. Second, I apply Bayesian inference for the uncertainty quantification of gas networks.

Additional affiliations

December 2015 - present

November 2014 - February 2015

September 2011 - December 2015

## Publications

Publications (24)

We consider the problem of efficient computations of the covariance matrix of the posterior probability density for linear Gaussian Bayesian inverse problems. When the probability density of the noise and the prior are Gaussian, the solution of such a statistical inverse problem is also Gaussian. Therefore, the underlying solution is characterized...

Optimal flow control problems are important for applications in science and engineering. Solving such problems usually requires the solution of a large linear generalized saddle-point system. This linear system is sparse and highly indefinite. In order to solve such systems using Krylov subspace methods, efficient preconditioners are necessary to e...

Multilevel sequentially semiseparable (MSSS) matrices form a class of structured matrices that have low-rank off-diagonal structure, which allows the matrix-matrix operations to be performed in linear computational complexity. MSSS preconditioners are computed by replacing the Schur complements in the block $LU$ factorization of the global linear s...

PDE-constrained optimization problems yield a linear saddle-point system that has to be solved. We propose a preconditioner that makes use of the global MSSS structure and a preconditioner that exploits the block MSSS structure of the saddle-point system. For the computation of preconditioners based on MSSS matrix computations, model order reductio...

This paper studies a new preconditioning technique for sparse systems arising from discretized partial differential equations in computational fluid dynamics problems. This preconditioning technique exploits the multilevel sequentially semiseparable (MSSS) structure of the system matrix. MSSS matrix computations give a data-sparse way to approximat...

In this article, we study preconditioning techniques for the control of the Navier–Stokes equation, where the control only acts on a few parts of the domain. Optimization, discretization, and linearization of the control problem results in a generalized linear saddle‐point system. The Schur complement for the generalized saddle‐point system is very...

We study the modeling and simulation of gas pipeline networks, with a focus on fast numerical methods for the simulation of transient dynamics. The obtained mathematical model of the underlying network is represented by a nonlinear differential algebraic equation (DAE). By introducing the concept of \textit{long pipes}, we can reduce the dimension...

We study the modeling and simulation of gas pipeline networks, with a focus on fast numerical methods for the simulation of transient dynamics. The obtained mathematical model of the underlying network is represented by a nonlinear differential algebraic equation (DAE). By introducing the concept of long pipes, we can reduce the dimension of the al...

In this work, we present a new numerical framework for the efficient solution of the time-harmonic elastic wave equation at multiple frequencies. We show that multiple frequencies (and multiple right-hand sides) can be incorporated when the discretized problem is written as a matrix equation. This matrix equation can be solved efficiently using the...

We consider the problem of estimating the uncertainty in statistical inverse problems using Bayesian inference. When the probability density of the noise and the prior are Gaussian, the solution of such a statistical inverse problem is also Gaussian. Therefore, the underlying solution is characterized by the mean and covariance matrix of the poster...

This paper presents a method to control chaotic behavior of a typical Smart Grid based on generalized fuzzy hyperbolic model (GFHM). As more and more distributed generations (DG) are incorporated into the Smart Grid, the chaotic behavior occurs increasingly. To verify the behavior, a dynamic model which describes a power system with DG is presented...

Saddle point systems arise in many applications, such as PDE-constrained optimization , computational fluid dynamics, electrical circuits and networks, e.t.c. [1]. Solving such systems is still a big challenge due to their indefiniteness and ill-conditioning. For large scale saddle point systems, Krylov methods are preferable. To speed up the conve...

The sequentially semiseparable (SSS) matrix approach provides an efficient framework for solving control and identification problems of one-dimensional (1-D) spatially interconnected systems. For this approach, model order reduction algorithm is essential for obtaining a low computational complexity. In this paper, we apply a novel model reduction...

This paper presents a class of preconditioners for sparse systems
arising from discretized partial differential equations (PDEs). In this
class of preconditioners, we exploit the multilevel sequentially
semiseparable (MSSS) structure of the system matrix. The off-diagonal
blocks of MSSS matrices are of low-rank, which enables fast computations
of l...

Sequentially Semi-Separable matrices are structured matrices with off-diagonal blocks of
low rank. By exploiting this structure, it is possible to formulate computationally efficient
algorithms for a wide range of problems.
In this poster, we present results for iterative solution algorithms for PDE-constrained
optimization problems. Discretization...

In this paper, we consider preconditioning for PDE-constrained optimization problems. The underlying problems yield a linear saddle-point system. We study a class of preconditioners based on multilevel sequentially semiseparable (MSSS) matrix computations. The novel global preconditioner is to make use of the global structure of the saddle-point sy...

As is well known, Smart Grid is a complex nonlinear system whose dynamical behavior has many complicated forms such as low frequency oscillation, subsynchronous oscillation and even chaos. Thus this paper focuses on the chaotic phenomenon of the widely concerned Smart Grid and presents its stabilization condition via establishing generalized fuzzy...