Publications related to A new method for solving broadband matching problems

MATHICSE Technical Report : A Multilevel Stochastic Gradient method for PDE-constrained Optimal Control Problems with uncertain parameters

Fabio Nobile, Matthieu Claude Martin, Panagiotis Tsilifis

In this paper, we present a multilevel Monte Carlo (MLMC) version of the Stochastic Gradient (SG) method for optimization under uncertainty, in order to tackle Optimal Control Problems (OCP) where the constraints are described in the form of PDEs with rand ...

MATHICSE2020

Frequency domain theory for functional time series: Variance decomposition and an invariance principle

Neda Mohammadi Jouzdani

This paper is concerned with frequency domain theory for functional time series, which are temporally dependent sequences of functions in a Hilbert space. We consider a variance decomposition, which is more suitable for such a data structure than the varia ...

INT STATISTICAL INST2020

Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI

Volkan Cevher, Ruud van Heeswijk, Baran Gözcü, Thomas Sanchez

Slow acquisition has been one of the historical problems in dynamic magnetic resonance imaging (dMRI), but the rise of compressed sensing (CS) has brought numerous algorithms that successfully achieve high acceleration rates. While CS proposes random sampl ...

2019

A new analysis approach for T-2 relaxometry myelin water quantification: Orthogonal Matching Pursuit

Guillaume Jean Op 't Veld

Purpose: In vivo myelin quantification can provide valuable noninvasive information on neuronal maturation and development, as well as insights into neurological disorders. Multiexponential analysis of multiecho T-2 relaxation is a powerful and widely appl ...

WILEY2019

Medium Frequency Transformer Leakage Inductance Modeling and Experimental Verification

Drazen Dujic, Marko Mogorovic

This paper provides detailed analytical modelling and finite elements method (FEM) analysis of the medium frequency transformer (MFT) leakage inductance, as one of the key design factors governing the operation of galvanically isolated power electronics co ...

2017

The recursive Hessian sketch for adaptive filtering

Martin Vetterli, Robin Scheibler

We introduce in this paper the recursive Hessian sketch, a new adaptive filtering algorithm based on sketching the same exponentially weighted least squares problem solved by the recursive least squares algorithm. The algorithm maintains a number of sketch ...

Ieee2016

Adaptive polynomial approximation by means of random discrete least squares

Giovanni Migliorati

We address adaptive multivariate polynomial approximation by means of the discrete least-squares method with random evaluations, to approximate in the L2 probability sense a smooth function depending on a random variable distributed according to a given pr ...

Springer2015

Analysis of resonant planar dissipative network antennas for rf inductively coupled plasma sources

Christoph Hollenstein, Alan Howling, Philippe Frédérique Bruno Guittienne

The analysis of a radio-frequency (RF) planar antenna is presented for applications in plasma processing. The antenna is a network of elementary meshes composed of inductive and capacitive elements which exhibits a set of resonant modes. The high currents ...

Iop Publishing Ltd2014

Resonant RF network antennas for large-area and large-volume inductively coupled plasma sources

Christoph Hollenstein, Alan Howling, Philippe Frédérique Bruno Guittienne

Large-area and large-volume radio frequency (RF) plasmas are produced by different arrangements of an elementary electrical mesh consisting of two conductors interconnected by a capacitor at each end. The obtained cylindrical and planar RF networks are res ...

Iop Publishing Ltd2013

Approximation of Quantities of Interest in Stochastic PDEs by the Random Discrete $L^2$ Projection on Polynomial Spaces

Fabio Nobile, Giovanni Migliorati, Erik Gustaf Bogislaw Von Schwerin

In this work we consider the random discrete

L^2

projection on polynomial spaces (hereafter RDP) for the approximation of scalar quantities of interest (QOIs) related to the solution of a partial differential equation model with random input parameters. ...

Siam Publications2013

A new method for solving broadband matching problems

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MATHICSE Technical Report : A Multilevel Stochastic Gradient method for PDE-constrained Optimal Control Problems with uncertain parameters

Frequency domain theory for functional time series: Variance decomposition and an invariance principle

Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI

A new analysis approach for T-2 relaxometry myelin water quantification: Orthogonal Matching Pursuit

Medium Frequency Transformer Leakage Inductance Modeling and Experimental Verification

The recursive Hessian sketch for adaptive filtering

Adaptive polynomial approximation by means of random discrete least squares

Analysis of resonant planar dissipative network antennas for rf inductively coupled plasma sources

Resonant RF network antennas for large-area and large-volume inductively coupled plasma sources

Approximation of Quantities of Interest in Stochastic PDEs by the Random Discrete $L^2$ Projection on Polynomial Spaces

A new analysis approach for T-2 relaxometry myelin water quantification: Orthogonal Matching Pursuit

The recursive Hessian sketch for adaptive filtering

Analysis of resonant planar dissipative network antennas for rf inductively coupled plasma sources

Frequency domain theory for functional time series: Variance decomposition and an invariance principle

MATHICSE Technical Report : A Multilevel Stochastic Gradient method for PDE-constrained Optimal Control Problems with uncertain parameters

Medium Frequency Transformer Leakage Inductance Modeling and Experimental Verification

Adaptive polynomial approximation by means of random discrete least squares

Resonant RF network antennas for large-area and large-volume inductively coupled plasma sources

Approximation of Quantities of Interest in Stochastic PDEs by the Random Discrete $L^2$ Projection on Polynomial Spaces

Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI