Concept

Laplacien discret

Publications associées (68)

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

Fredholm transformation on Laplacian and rapid stabilization for the heat equation

Shengquan Xiang

We study the rapid stabilization of the heat equation on the 1-dimensional torus using the backstepping method with a Fredholm transformation. This classical framework allows us to present the backstepping method with Fredholm transformations for the Lapla ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2022

SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators

Nathanaël Perraudin, Andreas Loukas, Karolis Martinkus

We approach the graph generation problem from a spectral perspective by first generating the dominant parts of the graph Laplacian spectrum and then building a graph matching these eigenvalues and eigenvectors. Spectral conditioning allows for direct model ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

A smooth basis for atomistic machine learning

Michele Ceriotti, Kevin Kazuki Huguenin-Dumittan, Filippo Bigi

Machine learning frameworks based on correlations of interatomic positions begin with a discretized description of the density of other atoms in the neighborhood of each atom in the system. Symmetry considerations support the use of spherical harmonics to ...

AIP Publishing2022

Annihilation Filter Approach For Estimating Graph Dynamics From Diffusion Processes

Pascal Frossard, Arun Venkitaraman

We propose an approach for estimating graph diffusion processes using annihilation filters from a finite set of observations of the diffusion process made at regular intervals. Our approach is based on the key observation that a graph diffusion process can ...

IEEE2022

Multiscale Representation Learning of Graph Data With Node Affinity

Pascal Frossard, Chenglin Li, Xing Gao

Graph neural networks have emerged as a popular and powerful tool for learning hierarchical representation of graph data. In complement to graph convolution operators, graph pooling is crucial for extracting hierarchical representation of data in graph neu ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Fredholm transformation on Laplacian and rapid stabilization for the heat equations

Shengquan Xiang

We revisit the rapid stabilization of the heat equation on the 1-dimensional torus using the backstepping method with a Fredholm transformation. We prove that, under some assumption on the control operator, two scalar controls are necessary and sufficient ...

2021

SiML: Sieved Maximum Likelihood for Array Signal Processing

Paul Hurley, Matthieu Martin Jean-André Simeoni

Stochastic Maximum Likelihood (SML) is a popular direction of arrival (DOA) estimation technique in array signal processing. It is a parametric method that jointly estimates signal and instrument noise by maximum likelihood, achieving excellent statistical ...

2021

MATHICSE Technical Report : Analysis-aware defeaturing: problem setting and a posteriori estimation

Annalisa Buffa, Rafael Vazquez Hernandez, Ondine Gabrielle Chanon

Defeaturing consists in simplifying geometrical models by removing the geometrical features that are considered not relevant for a given simulation. Feature removal and simplification of computer-aided design models enables faster simulations for engineeri ...

EPFL2021

On the Experimental Transferability of Spectral Graph Convolutional Networks

Spectral Graph Convolutional Networks (GCNs) are generalisations of standard convolutional for graph-structured data using the Laplacian operator. Recent work has shown that spectral GCNs have an intrinsic transferability. This work verifies this by studyi ...

2020

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