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DeepBND: A machine learning approach to enhance multiscale solid mechanics

Publications associées (149)

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Reduced order models for coupled systems

Niccolo' Discacciati

Efficient numerical simulations of coupled multi-component systems can be particularly challenging. This is mostly due to the complexity of their solutions, as mutual interactions may cause emergent behaviors, including synchronization and instabilities. V ...

EPFL2023

Benchmark exercise on image-based permeability determination of engineering textiles: Microscale predictions

Véronique Michaud, Baris Çaglar, Guillaume Clément Broggi

Permeability measurements of engineering textiles exhibit large variability as no standardization method currently exists; numerical permeability prediction is thus an attractive alternative. It has all advantages of virtual material characterization, incl ...

2023

An Elliptic Local Problem With Exponential Decay Of The Resonance Error For Numerical Homogenization

Assyr Abdulle, Doghonay Arjmand, Edoardo Paganoni

Numerical multiscale methods usually rely on some coupling between a macroscopic and a microscopic model. The macroscopic model is incomplete as effective quantities, such as the homogenized material coefficients or fluxes, are missing in the model. These ...

SIAM PUBLICATIONS2023

DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications

Pascal Frossard, Ádám Dániel Ivánkay

Along with the successful deployment of deep neural networks in several application domains, the need to unravel the black-box nature of these networks has seen a significant increase recently. Several methods have been introduced to provide insight into t ...

Assoc Computational Linguistics-Acl2023

Understanding how things break and slide is of paramount importance to describe the dynamics of a broad range of physical systems. This includes day-to-day problems such as the breaking of a glass of wine or the sliding of skis on snow, but also engineerin ...

EPFL2023

This paper investigates the theory of robustness against adversarial attacks. We focus on randomized classifiers (i.e. classifiers that output random variables) and provide a thorough analysis of their behavior through the lens of statistical learning theo ...

SPRINGER2022

A Ribbon Model for Nematic Polymer Networks

Harmeet Singh

We present a theory of deformation of ribbons made of nematic polymer networks (NPNs). These materials exhibit properties of rubber and nematic liquid crystals, and can be activated by external stimuli of heat and light. A two-dimensional energy for a shee ...

SPRINGER2022

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We present a numerical assessment of the thermal-hydraulic performance of the NbTi bus bars designed for the European DEMO. We consider the CS, TF and PF bus bars, as well as two alternative TF options for high-current operations. The main sources of stati ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Mechanical performance and upscaling of bio-improved soils

Ray Harran

Ecological awareness, pressing climate considerations and rising need for soil stabilization push forward the quest for alternative solutions in geotechnical engineering such as Microbially Induced Carbonate Precipitation (MICP). Precipitated crystals serv ...

EPFL2022

Microscale hydrodynamic confinements: shaping liquids across length scales as a toolbox in life sciences

Philippe Renaud, David Philipp Taylor, Prerit Mathur

Hydrodynamic phenomena can be leveraged to confine a range of biological and chemical species without needing physical walls. In this review, we list methods for the generation and manipulation of microfluidic hydrodynamic confinements in free-flowing liqu ...

ROYAL SOC CHEMISTRY2022