Publication

Signification of the phase function parameter gamma explored with a fractal distribution of scatterers

Publications associées (31)

Generalization of Quasi-Newton Methods: Application to Robust Symmetric Multisecant Updates

Quasi-Newton (qN) techniques approximate the Newton step by estimating the Hessian using the so-called secant equations. Some of these methods compute the Hessian using several secant equations but produce non-symmetric updates. Other quasi-Newton schemes, ...

MICROTOME PUBLISHING2021

Time-Synchronization Attacks against Critical Infrastructures and their Mitigation

Marguerite Marie Nathalie Delcourt

This work focuses on the security of critical infrastructures against time-synchronization attacks (TSA). A TSA can impact any network that relies on the dynamic analysis of data, by altering the time synchronization between its nodes. Such attacked networ ...

EPFL2021

TDOA Source-Localization Technique Robust to Time-Synchronization Attacks

Jean-Yves Le Boudec, Marguerite Marie Nathalie Delcourt

In this paper, we focus on the localization of a passive source from time difference of arrival (TDOA) measurements. TDOA values are computed with respect to pairs of fixed sensors that are required to be accurately time-synchronized. This constitutes a we ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Machine learning models of the energy curvature vs particle number for optimal tuning of long-range corrected functionals

Anne-Clémence Corminboeuf, Alberto Fabrizio, Benjamin André René Meyer

The average energy curvature as a function of the particle number is a molecule-specific quantity, which measures the deviation of a given functional from the exact conditions of density functional theory. Related to the lack of derivative discontinuity in ...

AMER INST PHYSICS2020

Learning (from) the Electron Density: Transferability, Conformational and Chemical Diversity

Anne-Clémence Corminboeuf, Alberto Fabrizio, Andrea Grisafi, Ksenia Briling

Machine-learning in quantum chemistry is currently booming, with reported applications spanning all molecular properties from simple atomization energies to complex mathematical objects such as the many-body wavefunction. Due to its central role in density ...

2020

Structure-property maps with Kernel principal covariates regression

Michele Ceriotti, Benjamin Aaron Helfrecht, Guillaume André Jean Fraux

Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building supervised or unsupervised machine learning models. Principal covariates regression (PC ...

2020

PMU-based linear state estimation of Lausanne subtransmission network: Experimental validation

Sidi-Rachid Cherkaoui, Mario Paolone, Paolo Romano, Marco Pignati, Lorenzo Zanni, Asja Derviskadic, Ali Abur

The paper presents the implementation details and the experimental validation of a linear state estimator based on synchrophasor measurements in a real subtransmission network. The data are provided by 15 phasor measurement units (PMUs) installed in the 12 ...

Elsevier2020

High-order based revelation of bifurcation of novel Schatz-inspired metamorphic mechanisms using screw theory

Huijuan Feng

The revelation of mechanism bifurcation is essential in the design and analysis of reconfigurable mechanisms. The first- and second-order based methods have successfully revealed the bifurcation of mechanisms. However, they fail in the novel Schatz-inspire ...

PERGAMON-ELSEVIER SCIENCE LTD2020

Applying SHARK-X to perform data assimilation with the LWR-PROTEUS Phase II integral experiments

Andreas Pautz, Daniel Jerôme Siefman, Mathieu Hursin

Data assimilation methods have recently been implemented in the sensitivity analysis and uncertainty quantification tool SHARK-X. This paper presents how they were used with sensitivity coefficients and randomly-sampled nuclear data to simulate integral pa ...

PERGAMON-ELSEVIER SCIENCE LTD2020

Dynamics and clogging of colloidal monolayers magnetically driven through a heterogeneous landscape

Ignacio Pagonabarraga Mora

We combine experiments and numerical simulations to investigate the emergence of clogging in a system of interacting paramagnetic colloidal particles driven against a disordered landscape of larger obstacles. We consider a single aperture in a landscape of ...

ROYAL SOC CHEMISTRY2020

Wasserstein Distributionally Robust Learning

Soroosh Shafieezadeh Abadeh

Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...

EPFL2020