Publications associées à Décomposition de Schur

Experiments and gyrokinetic simulations of TCV plasmas with negative triangularity in view of DTT operations

Olivier Sauter, Stefano Coda, Justin Richard Ball, Alberto Mariani, Matteo Vallar, Filippo Bagnato

Negative triangularity (NT) scenarios in TCV have been compared to positive triangularity (PT) scenarios using the same plasma shapes foreseen for divertor tokamak test tokamak operations. The experiments provided a NT/PT L-mode pair and a PT H-mode with d ...

Iop Publishing Ltd2024

NBI optimization on SMART and implications for scenario development

Mario Ludovico Podesta, Francesca Maria Poli

The SMall Aspect Ratio Tokamak (SMART) under commissioning at the University of Seville, Spain, aims to explore confinement properties and possible advantages in confinement for compact/spherical tokamaks operating at negative vs. positive triangularity. T ...

Iop Publishing Ltd2024

espm: A Python library for the simulation of STEM-EDXS datasets

Cécile Hébert, Duncan Thomas Lindsay Alexander, Hui Chen, Guillaume Romain Obozinski, Nathanaël Perraudin

We present two open-source Python packages: "electron spectro-microscopy"(espm) and "electron microscopy tables"(emtables). The espm software enables the simulation of scanning transmission electron microscopy energy-dispersive X-ray spectroscopy datacubes ...

ELSEVIER2023

Singular quadratic eigenvalue problems: linearization and weak condition numbers

Daniel Kressner, Ivana Sain Glibic

The numerical solution of singular eigenvalue problems is complicated by the fact that small perturbations of the coefficients may have an arbitrarily bad effect on eigenvalue accuracy. However, it has been known for a long time that such perturbations are ...

SPRINGER2023

Green Cryptography and Other Optimisations

Andrea Felice Caforio

The spectral decomposition of cryptography into its life-giving components yields an interlaced network of tangential and orthogonal disciplines that are nonetheless invariably grounded by the same denominator: their implementation on commodity computing p ...

EPFL2023

Iterative refinement of Schur decompositions

Daniel Kressner, Zvonimir Bujanovic

The Schur decomposition of a square matrix A is an important intermediate step of state-of-the-art numerical algorithms for addressing eigenvalue problems, matrix functions, and matrix equations. This work is concerned with the following task: Compute a (m ...

SPRINGER2023

Decomposition Strategy for Districts as Renewable Energy Hubs

François Maréchal, Cédric Terrier, Luise Middelhauve

In light of the energy transition, it becomes a widespread solution to decentralize and to decarbonize energy systems. However, limited transformer capacities are a hurdle for large-scale integration of solar energy in the electricity grid. The aim of this ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Enhanced confinement in diverted negative-triangularity L-mode plasmas in TCV

Olivier Sauter, Stefano Coda, Benoît Labit, Laurie Porte, Alessandro Pau, Alessandro Marinoni, Antoine Pierre Emmanuel Alexis Merle, Umar Sheikh, Cedric Kar-Wai Tsui, Matteo Vallar

The favorable confinement properties of negative-triangularity (NT) tokamak configurations were discovered in the TCV tokamak in the late 1990s and were documented over the two following decades, through investigations of predominantly electron-heated plas ...

2022

Logic Resynthesis of Majority-Based Circuits by Top-Down Decomposition

Giovanni De Micheli, Heinz Riener, Siang-Yun Lee

Logic resynthesis is the problem of finding a dependency function to re-express a given Boolean function in terms of a given set of divisor functions. In this paper, we study logic resynthesis of majority-based circuits, which is motivated by the increasin ...

IEEE2021

Kinetic Euclidean Distance Matrices

Martin Vetterli, Ivan Dokmanic, Puoya Tabaghi

Euclidean distance matrices (EDMs) are a major tool for localization from distances, with applications ranging from protein structure determination to global positioning and manifold learning. They are, however, static objects which serve to localize point ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020