Publications associées à Asynchronous Diffusion Learning with Agent Subsampling and Local Updates

Publications associées (14)

Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

Jérôme Chenal, Baraka Jean-Claude Munyaka

This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (US ...

AIP Publishing2025

Optimized Quantification of Multiple Drug Concentrations by WeightedMSE With Machine Learning on Electrochemical Sensor

Sandro Carrara, Francesca Rodino, Lin Du, Tatsunori Matsumoto

Quantification of multiple drugs is of great importance and urgently needed in therapeutic drug monitoring (TDM) and personalized therapy. Especially, based on cyclic voltammograms (CVs) obtained by electrochemical sensors, the use of artificial neural net ...

2024

How Stress Barriers and Fracture Toughness Heterogeneities Arrest Buoyant Hydraulic Fractures

Brice Tanguy Alphonse Lecampion, Carlo Peruzzo, Andreas Möri

In our study, we investigated the impact of changes in Mode I fracture toughness and stress barriers on fully developed planar, buoyant hydraulic fractures assuming linear elastic hydraulic fracture mechanics. We present scaling-based arguments to predict ...

Springer Wien2024

An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0

Adrien Michel

Many methods exist to model snow densification in order to calculate the depth of a single snow layer or the depth of the total snow cover from its mass. Most of these densification models need to be tightly integrated with an accumulation and melt model a ...

COPERNICUS GESELLSCHAFT MBH2023

Stabilized isogeometric discretizations on trimmed and union geometries, and weak imposition of the boundary conditions for the Darcy flow

Riccardo Puppi

Modern manufacturing engineering is based on a ``design-through-analysis'' workflow. According to this paradigm, a prototype is first designed with Computer-aided-design (CAD) software and then finalized by simulating its physical behavior, which usually i ...

EPFL2022

From coating flow patterns to porous body wake dynamics via multiscale models

Pier Giuseppe Ledda

Multiscale phenomena are involved in countless problems in fluid mechanics. Coating flows are known to exhibit a broad variety of patterns, such as wine tears in a glass and dripping of fresh paint applied on a wall. Coating flows are typically modeled und ...

EPFL2022

Uncertainty modeling of household appliance loads for smart energy management

Jean-Louis Scartezzini

Households represent a large share of flexibility on the demand side for assisting the smooth operation of the highly renewable grid. The stochastic nature of activities and various appliance usage patterns of household occupants affects the flexibility po ...

ELSEVIER2021

Novel corrector problems with exponential decay of the resonance error for numerical homogenization

Edoardo Paganoni

Multiscale problems, such as modelling flows through porous media or predicting the mechanical properties of composite materials, are of great interest in many scientific areas. Analytical models describing these phenomena are rarely available, and one mus ...

EPFL2020

Prediction of optically-triggered amplification in phototransistor with SPICE circuit simulators

Jean-Michel Sallese, Chiara Rossi

Modeling of optoelectronic devices involves long and complex numerical simulations, usually performed with TCAD tools. Numerical simulations provide accurate results for a single device but are not feasible when dealing with a full circuit comprising sever ...

SPIE-INT SOC OPTICAL ENGINEERING2020

Large eddy simulation of the tip-leakage cavitating flow with an insight on how cavitation influences vorticity and turbulence

Mohamed Farhat

Cavitation within a tip leakage flow remains a challenging issue in a variety of axial hydraulic machines. It is still not possible nowadays to predict cavitation occurrence in such a flow with acceptable accuracy. In the present study, we have carried out ...

ELSEVIER SCIENCE INC2020

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

Time-lapse reconstruction of the fracture front from diffracted waves arrivals in laboratory hydraulic fracture experiments

Brice Tanguy Alphonse Lecampion, Dong Liu, Thomas Emmanuel Blum

4-D acoustic imaging via an array of 32 sources/32 receivers is used to monitor hydraulic fracture (HF) propagating in a 250 mm cubic specimen under a true-triaxial state of stress. We present a method based on the arrivals of diffracted waves to reconstru ...

2020

From bulk descriptions to emergent interfaces: Connecting the Ginzburg-Landau and elastic-line models

Elisabeth Agoritsas

Controlling interfaces is highly relevant from a technological point of view. However, their rich and complex behavior makes them very difficult to describe theoretically, and hence to predict. In this work, we establish a procedure to connect two levels o ...

2020

Context effects on probability estimation

Wei-Hsiang Lin

Many decisions rely on how we evaluate potential outcomes and estimate their corresponding probabilities of occurrence. Outcome evaluation is subjective because it requires consulting internal preferences and is sensitive to context. In contrast, probabili ...

PUBLIC LIBRARY SCIENCE2020