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Concept

Field extension

Related publications (27)

Roadmap on Machine learning in electronic structure

Michele Ceriotti, Andrea Grisafi, Max David Veit, Jigyasa Nigam

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by ...

IOP Publishing Ltd2022

The Birth of a New Field: Memristive Sensors. A Review

Sandro Carrara

Over the last 11 years, the field of Memristive Sensors abruptly emerged in literature. The present review paper has the aim to acknowledge such an exciting new field of research as well as to critically review its most significant outcomes. As usual in sc ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Computation Of A 30750-Bit Binary Field Discrete Logarithm

Arjen Lenstra, Thorsten Kleinjung, Robert Granger, Benjamin Pierre Charles Wesolowski

This paper reports on the computation of a discrete logarithm in the finite field F-230750, breaking by a large margin the previous record, which was set in January 2014 by a computation in F-29234. The present computation made essential use of the elimina ...

AMER MATHEMATICAL SOC2021

Role of electric currents in the Fano resonances of connected plasmonic structures

Olivier Martin, Marco Riccardi

In this work, we use finite elements simulations to study the far field properties of two plasmonic structures, namely a dipole antenna and a cylinder dimer, connected to a pair of nanorods. We show that electrical, rather than near field, coupling between ...

2021

Remarks on Poincare and interpolation estimates for Truncated Hierarchical B-splines

Annalisa Buffa

This paper should be considered as an addendum to [A. Buffa and C. Giannelli, Adaptive isogeometric methods with hierarchical splines: Error estimator and convergence, Math. Models Methods Appl. Sci. 26 (2016) 1-25] and [A. Buffa and C. Giannelli, Adaptive ...

2021

Gaussian Process Regression for Materials and Molecules

Michele Ceriotti, David Mark Wilkins

We provide an introduction to Gaussian process regression (GPR) machinelearning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of in ...

AMER CHEMICAL SOC2021

Emotions in Engineering Education: Preliminary Results from a Scoping Review

Roland John Tormey

There is today a broad consensus that emotions influence all forms of teaching and learning, and scholarship on Emotions in Engineering Education (EEE) is an emerging and rapidly growing field. However, this nascent research is currently very dispersed and ...

2021