Concept

Degrees of freedom (statistics)

Related publications (66)

Outlier-free spline spaces for isogeometric discretizations of biharmonic and polyharmonic eigenvalue problems

We present outlier-free isogeometric Galerkin discretizations of eigenvalue problems related to the biharmonic and the polyharmonic operator in the univariate setting. These are Galerkin discretizations in certain spline subspaces that provide accurate app ...

Lausanne2023

Application of optimal spline subspaces for the removal of spurious outliers in isogeometric discretizations

Espen Sande

We show that isogeometric Galerkin discretizations of eigenvalue problems related to the Laplace operator subject to any standard type of homogeneous boundary conditions have no outliers in certain optimal spline subspaces. Roughly speaking, these optimal ...

ELSEVIER SCIENCE SA2022

Comparison of non-parametric T2 relaxometry methods for myelin water quantification

Jean-Philippe Thiran, Tobias Kober, Tom Hilbert, Erick Jorge Canales Rodriguez, Marco Pizzolato, Gian Franco Piredda, Thomas Yu, Alessandro Daducci, Nicolas Kunz

Multi-component T2 relaxometry allows probing tissue microstructure by assessing compartment-specific T2 relaxation times and water fractions, including the myelin water fraction. Non-negative least squares (NNLS) with zero-order Tikhonov regularization is ...

2021

Stability and Unbalance Analysis of Rigid Rotors Supported by Spiral Groove Bearings: Comparison of Different Approaches

Jürg Alexander Schiffmann, Elia Iseli

The dynamic behavior of spiral-grooved gas bearing supported four degrees-of-freedom (DOF) rotors is investigated by means of linearized bearing force coefficients and full time-integrated transient analysis. The transient method consists of a state-space ...

2021

Physics-informed machine learning for reduced-order modeling of nonlinear problems

Qian Wang, Jan Sickmann Hesthaven

A reduced basis method based on a physics-informed machine learning framework is developed for efficient reduced-order modeling of parametrized partial differential equations (PDEs). A feedforward neural network is used to approximate the mapping from the ...

2021

Multi-View Shape Estimation of Transparent Containers

Pascal Frossard, Andrea Cavallaro, Apostolos Modas

The 3D localisation of an object and the estimation of its properties, such as shape and dimensions, are challenging under varying degrees of transparency and lighting conditions. In this paper, we propose a method for jointly localising container-like obj ...

2020

Physics-informed machine learning for reduced-order modeling of nonlinear problems

Qian Wang, Jan Sickmann Hesthaven

A physics-informed machine learning framework is developed for the reduced-order modeling of parametrized steady-state partial differential equations (PDEs). During the offline stage, a reduced basis is extracted from a collection of high-fidelity solution ...

2020

Joint estimation of RETF vector and power spectral densities for speech enhancement based on alternating least squares

Ina Kodrasi

The multi-channel Wiener filter (MWF) is a well-known multi-microphone speech enhancement technique, aiming at improving the quality of the recorded speech signals in noisy and reverberant environments. Assuming that reverberation and ambient noise can be ...

2019

Change of beam distribution due to decoherence in the presence of transverse feedback

Xavier Buffat, Sondre Vik Furuseth

The effect of Landau damping is often calculated based on a Gaussian beam distribution in all degrees of freedom. The stability of the beam is however strongly dependent on the details of the distribution. The present study focuses on the change of bunch d ...

IOP PUBLISHING LTD2019

A Conditional Gradient-Based Augmented Lagrangian Framework

Volkan Cevher, Alp Yurtsever

This paper considers a generic convex minimization template with affine constraints over a compact domain, which covers key semidefinite programming applications. The existing conditional gradient methods either do not apply to our template or are too slow ...

2019

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