Concept

Computational complexity

Publications associées (1 000)

A detailed view on the rebar–to–concrete interaction based on refined measurement techniques

Aurelio Muttoni, Miguel Fernández Ruiz, Raffaele Cantone

Simplified assumptions have traditionally been made on the rebar–to–concrete interaction in structural concrete to calculate the stress state in rebars. Such assumptions typically consider a uniform stress state in the reinforcement at a given cross sectio ...

2020

A simple, versatile and robust centrifugation-based filtration protocol for the isolation and quantification of alpha-synuclein monomers, oligomers and fibrils: Towards improving experimental reproducibility in alpha-synuclein research

Hilal Lashuel, Anass Chiki, Sonia Donzelli, Muhammed Muazzam Kamil Syed

Increasing evidence suggests that the process of alpha-synuclein (alpha-syn) aggregation from monomers into amyloid fibrils and Lewy bodies, via oligomeric intermediates plays an essential role in the pathogenesis of different synucleinopathies, including ...

WILEY2020

Assessing the Efficacy of a GPU-Based MW-FDTD Method for Calculating Lightning Electromagnetic Fields Over Large-Scale Terrains

Marcos Rubinstein, Mohammad Azadifar, Farhad Rachidi-Haeri, Hamidreza Karami

This article presents for the first time an OpenACC (Open accelerators)-aided Graphics Processing Unit (GPU)-based approach adopting a 3D moving window finite difference time domain (MW-FDTD) method for calculating lightning electromagnetic fields over lar ...

2020

Artificial Neural Networks as Surrogate Models for Uncertainty Quantification and Data Assimilation in 2-D/3-D Fuel Performance Studies

Andreas Pautz, Mathieu Hursin, Carlo Fiorina, Alessandro Scolaro, Daniel Jerôme Siefman

This paper preliminarily investigates the use of data-driven surrogates for fuel performance codes. The objective is to develop fast-running models that can be used in the frame of uncertainty quantification and data assimilation studies. In particular, da ...

2020

Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Guillermo Ortiz Jimenez, Apostolos Modas

Driven by massive amounts of data and important advances in computational resources, new deep learning systems have achieved outstanding results in a large spectrum of applications. Nevertheless, our current theoretical understanding on the mathematical fo ...

2020

On the usefulness of mixed logit models with unobserved inter-and intra-individual heterogeneity

Michel Bierlaire, Prateek Bansal

Mixed logit models with unobserved inter- and intra-individual heterogeneity hierarchically extend standard mixed logit models by allowing tastes to vary randomly both across individuals as well as across choice tasks encountered by the same individual. Re ...

EPFL2020

Multimodal water age distributions and the challenge of complex hydrological landscapes

Paolo Benettin

Travel time distributions (TTDs) are concise descriptions of transport processes in catchments based on water ages, and they are particularly efficient as lumped hydrological models to simulate tracers in outflows. Past studies have approximated catchment ...

2020

Contrast-Enhanced Ultrasound Quantification: From Kinetic Modeling To Machine Learning

Marcel Arditi

Ultrasound contrast agents (UCAs) have opened up immense diagnostic possibilities by combined use of indicator dilution principles and dynamic contrast-enhanced ultrasound (DCE-US) imaging. UCAs are microbubbles encapsulated in a biocompatible shell. With ...

2020

Active illumination and computational Methods for Temporal and Spectral Super-Resolution Microscopy

Christian Jaques

Light microscopy is a tool of paramount importance for biologists and has been constantly improved for the past four centuries. Despite many recent developments, microscopy techniques still require improvement, especially to reach better temporal and spect ...

EPFL2020

A note on efficient computation of privileged directions in modifier adaptation

Dominique Bonvin, Timm Faulwasser, Alejandro Gabriel Marchetti, Martand Singhal

In the presence of plant-model mismatch, the estimation of plant gradients is key to the performance of measurement-based iterative optimization schemes. However, gradient estimation requires time-consuming experiments, wherein the plant is sequentially pe ...

PERGAMON-ELSEVIER SCIENCE LTD2020

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