Publications related to Geometric mean

Strain-based analysis on the influence of local undercut geometry on fatigue crack initiation life

Alain Nussbaumer, Heikki Tapani Remes, Abinab Niraula

The local undercut defects at the weld toe provide a potential initiation site for fatigue cracks and significantly impact the structure's fatigue strength. The influence of continuous undercut depth on fatigue performance is widely studied, but the resear ...

2024

Divertor detachment and reattachment with mixed impurity seeding on ASDEX Upgrade

Olivier Claude Martin Février

Using newly developed spectroscopic models to measure the divertor concentration of Ne and Ar, it is shown that the experimental detachment threshold on ASDEX Upgrade with Ar-only and mixtures of Ar+N or Ne+N scales as expected in comparison with an analyt ...

IOP Publishing Ltd2023

Grassmannian diffusion maps based surrogate modeling via geometric harmonics

Ketson Roberto Maximiano Dos Santos

A novel surrogate model based on the Grassmannian diffusion maps (GDMaps) and utilizing geometric harmonics (GH) is developed for predicting the response of complex physical phenomena. The method utilizes GDMaps to obtain a low-dimensional representation o ...

Wiley2022

From Trees to Barcodes and Back Again: A Combinatorial, Probabilistic and Geometric Study of a Topological Inverse Problem

Adélie Eliane Garin

In this thesis, we investigate the inverse problem of trees and barcodes from a combinatorial, geometric, probabilistic and statistical point of view. Computing the persistent homology of a merge tree yields a barcode B. Reconstructing a tree from B invol ...

EPFL2022

Genuinely distributed Byzantine machine learning

Rachid Guerraoui, El Mahdi El Mhamdi, Le Nguyen Hoang, Sébastien Louis Alexandre Rouault, Arsany Hany Abdelmessih Guirguis

Machine learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component ...

2022

Affine Combination of Diffusion Strategies Over Networks

Ali H. Sayed, Jie Chen

Diffusion adaptation is a powerful strategy for distributed estimation and learning over networks. Motivated by the concept of combining adaptive filters, this work proposes a combination framework that aggregates the operation of multiple diffusion strate ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Variance-Reduced Stochastic Learning Under Random Reshuffling

Ali H. Sayed, Bicheng Ying, Kun Yuan

Several useful variance-reduced stochastic gradient algorithms, such as SVRG, SAGA, Finito, and SAG, have been proposed to minimize empirical risks with linear convergence properties to the exact minimizer. The existing convergence results assume uniform d ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Finite beta effects on short wavelength ion temperature gradient modes

Laurent Villard

The electromagnetic effect is studied on the short wavelength branch of the ion temperature gradient mode in the linear regime for the first time using a global gyrokinetic model. The short wavelength ion temperature gradient mode growth rate is found to b ...

2020

Random time step probabilistic methods for uncertainty quantification in chaotic and geometric numerical integration

Assyr Abdulle, Giacomo Garegnani

A novel probabilistic numerical method for quantifying the uncertainty induced by the time integration of ordinary differential equations (ODEs) is introduced. Departing from the classical strategy to randomize ODE solvers by adding a random forcing term, ...

2020

The Role of Reynolds Number Effect and Tip Leakage in Compressor Geometry Scaling at Low Turbulent Reynolds Numbers

Jürg Alexander Schiffmann, Markus Diehl, Christoph Schreiber

In compressor design, a convenient way to save time is to scale an existing geometry to required specifications, rather than developing a new design. The approach works well when scaling compressors of similar size at high Reynolds numbers but becomes more ...

2020

Kernel Density Estimation through Density Constrained Near Neighbor Search

Mikhail Kapralov, Navid Nouri

In this paper we revisit the kernel density estimation problem: given a kernel K(x, y) and a dataset of n points in high dimensional Euclidean space, prepare a data structure that can quickly output, given a query q, a (1 + epsilon)-approximation to mu := ...

IEEE2020

Increasing risk: Dynamic mean-preserving spreads

Max-Olivier Hongler

We extend the celebrated Rothschild and Stiglitz (1970) definition of Mean-Preserving Spreads to a dynamic framework. We adapt the original integral conditions to transition probability densities, and give sufficient conditions for their satisfaction. We t ...

2020

Online Distributed Learning Over Graphs With Multitask Graph-Filter Models

Ali H. Sayed, Roula Nassif

In this article, we are interested in adaptive and distributed estimation of graph filters from streaming data. We formulate this problem as a consensus estimation problem over graphs, which can be addressed with diffusion LMS strategies. Most popular grap ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Polynomial Escape-Time From Saddle Points In Distributed Non-Convex Optimization

Ali H. Sayed, Stefan Vlaski

The diffusion strategy for distributed learning from streaming data employs local stochastic gradient updates along with exchange of iterates over neighborhoods. In this work we establish that agents cluster around a network centroid in the mean-fourth sen ...

IEEE2019

Comment on “Origin of water in the Badain Jaran Desert, China: New insight from isotopes” by Wu et al. (2017)

David Andrew Barry, Ho Ling Li, Jiaqi Chen

Precipitation isotope data were used to determine the origin of groundwater in the Badain Jaran Desert (BJD) in the study of Wu et al. (2017). Both precipitation and its isotope composition vary seasonally, so arithmetic averages of precipitation isotope v ...

2018

Periodic Splines and Gaussian Processes for the Resolution of Linear Inverse Problems

Michaël Unser, Julien René Pierre Fageot, Anaïs Laure Marie-Thérèse Badoual

This paper deals with the resolution of inverse problems in a periodic setting or, in other terms, the reconstruction of periodic continuous-domain signals from their noisy measurements. We focus on two reconstruction paradigms: variational and statistical ...

2018

Locality of the anomalous Hall conductivity

Antimo Marrazzo

The geometrical intrinsic contribution to the anomalous Hall conductivity (AHC) of a metal is commonly expressed as a reciprocal-space integral: as such, it only addresses unbounded and macroscopically homogeneous samples. Here we show that the geometrical ...

Amer Physical Soc2017

Methods and Approaches for Blind Test Predictions of Out-Of-Plane Behavior of Masonry Walls: A Numerical Comparative Study

Katrin Beyer, Andrea Penna

Earthquakes cause severe damage to masonry structures due to inertial forces acting in the normal direction to the plane of the walls. The out-of-plane behavior of masonry walls is complex and depends on several parameters, such as material and geometric p ...

2017

STEP-NC compliant process planning of additive manufacturing: remanufacturing

Ian Anthony Stroud, Jumyung Um

Additive manufacturing is becoming one of the key methods for reproducing repair sections in remanufacturing processes. The major advantage of using additive processes is to minimize production time and waste. However, the surface quality and shape accurac ...

Springer London Ltd2017

Evaluation of the shape deviation of non rigid parts from optical measurements

Loïc Andolfatto

This paper deals with an approach to identify geometrical deviations of flexible parts from optical measurements. Each step of the approach defines a specific issue which we try to respond to. The problem of measurement uncertainties is solved using an ori ...

Springer Verlag2017