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Machine-Learning of Atomic-Scale Properties Based on Physical Principles

Publications associées (39)

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A kernel compression scheme for fractional differential equations

Jan Sickmann Hesthaven, Daniel Henri Baffet

The nonlocal nature of the fractional integral makes the numerical treatment of fractional dierential equations expensive in terms of computational eort and memory requirements. In this paper we propose a method to reduce these costs while controlling the ...

Society for Industrial and Applied Mathematics2017

On the lower semicontinuous envelope of functionals defined on polyhedral chains

Maria Colombo

In this note we prove an explicit formula for the lower semicontinuous envelope of some functionals defined on real polyhedral chains. More precisely, denoting by H:R→[0,∞) an even, subadditive, and lower semicontinuous function with H(0)=0, and by ΦH the ...

2017

Impact of dispersion forces on the atomic structure of a prototypical network-forming disordered system: The case of liquid GeSe2

Assil Bouzid, Carlo Massobrio

Aset of structural properties of liquid GeSe2 are calculated by using first-principles molecular dynamics and including, for the first time, van derWaals dispersion forces. None of the numerous atomic-scale simulations performed in the past on this prototy ...

Amer Inst Physics2017

Enumeration of reversible functions and its application to circuit complexity

Giovanni De Micheli, Mathias Soeken, Nabila Abdessaied

We review combinational results to enumerate and classify reversible functions and investigate the application to circuit complexity. In particularly, we consider the effect of negating and permuting input and output variables and the effect of applying li ...

Springer2016

A New Formulation of the Cooray-Rubinstein Expression in Time Domain

In this paper, a new formulation in the time domain of the Cooray-Rubinstein expression is presented. This new formulation overcomes the problems related to the previous formulation: The Dirac pulse contained in the kernel of the previous formulation is ex ...

Ieee-Inst Electrical Electronics Engineers Inc2015

Sparsity and Infinite Divisibility

Michaël Unser, Arash Amini

We adopt an innovation-driven framework and investigate the sparse/compressible distributions obtained by linearly measuring or expanding continuous-domain stochastic models. Starting from the first principles, we show that all such distributions are neces ...

2014

There has been increasing interest in applying learning algorithms to improve the dexterity of myoelectric prostheses. In this work, we present a large-scale benchmark evaluation on the second iteration of the publicly released NinaPro database, which cont ...

2014

A constraint sampling approach for multi-stage robust optimization

Daniel Kuhn

We propose a tractable approximation scheme for convex (not necessarily linear) multi-stage robust optimization problems. We approximate the adaptive decisions by finite linear combinations of prescribed basis functions and demonstrate how one can optimize ...

Elsevier2012

NURBS-Based BEM Implementation of High-Order Surface Impedance Boundary Conditions

Annalisa Buffa, Rafael Vazquez Hernandez

When implementing high-order surface impedance boundary conditions in collocation boundary element method (BEM) with constant or linear elements, difficulties arise due to the computation of the curvature of the conductors and of the tangential derivatives ...

2012

Subspaces Indexing Model on Grassmann Manifold for Image Search

Xinchao Wang

Conventional linear subspace learning methods like principal component analysis (PCA), linear discriminant analysis (LDA) derive subspaces from the whole data set. These approaches have limitations in the sense that they are linear while the data distribut ...

Institute of Electrical and Electronics Engineers2011