Graph neural networks for dynamic modeling of roller bearings

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We examine the connection of two graph parameters, the size of a minimum feedback arcs set and the acyclic disconnection. A feedback arc set of a directed graph is a subset of arcs such that after deletion the graph becomes acyclic. The acyclic disconnecti ...

Spectral algorithms are some of the main tools in optimization and inference problems on graphs. Typically, the graph is encoded as a matrix and eigenvectors and eigenvalues of the matrix are then used to solve the given graph problem. Spectral algorithms ...

We study the global well-posedness and asymptotic behavior for a semilinear damped wave equation with Neumann boundary conditions, modeling a one-dimensional linearly elastic body interacting with a rigid substrate through an adhesive material. The key fea ...

This work focuses on the coupling of trimmed shell patches using Isogeometric Analysis, based on higher continuity splines that seamlessly meet the

C^1

requirement of Kirchhoff-Love-based discretizations. Weak enforcement of coupling conditions is achiev ...

We propose a local, non -intrusive model order reduction technique to accurately approximate the solution of coupled multi -component parametrized systems governed by partial differential equations. Our approach is based on the approximation of the boundar ...

We study the hitting probabilities of the solution to a system of d stochastic heat equations with additive noise subject to Dirichlet boundary conditions. We show that for any bounded Borel set with positive (d-6)\documentclass[12pt]{minimal} \usepackage{ ...

In the attempt to reduce fuel consumption, a new generation of Ultra-High-By-Pass-Ratio (UHBR) turbofans have been introduced in the aeronautic industry which are structurally noisier especially at lower frequencies, because of their larger diameter, lower ...

Gas bearings use pressurized gas as a lubricant to support and guide rotating machinery. These bearings have a number of advantages over traditional lubricated bearings, including higher efficiency in a variety of applications and reduced maintenance requi ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

A luminescent solar concentrator (LSC) offers a viable solution to spectrally convert and concentrate both direct and diffuse sunlight without the need for tracking. Its potential for commercialization is currently limited by the optical performance. A det ...

Translation elongation plays an important role in regulating protein concentrations in the cell, and dysregulation of this process has been linked to several human diseases. In this study, we use data from ribo-seq experiments to model ribosome densities, ...

Plasmas with negative triangularity (NT) shape have been recently shown to be able to achieve H-mode levels of confinement in L-mode, avoiding detrimental edge localised modes. Therefore, this plasma geometry is now studied as a possible viable option for ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Rubble stone masonry is a common construction typology of historical city centres and vernacular architecture. While past earthquakes have shown that it is one of the most vulnerable masonry construction typologies, there are few experimental campaigns giv ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

The construction industry plays a major role in the high levels of greenhouse gas emissions, resource consumption, and waste generation observed nowadays. Key to the circular economy, structural component reuse arises as a promising solution to divert cons ...

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, ...

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

Graph neural networks for dynamic modeling of roller bearings

Chat with Graph Search

The connection of the acyclic disconnection and feedback arc sets - On an open problem of Figueroa et al

The Power of Two Matrices in Spectral Algorithms for Community Recovery

Exponential convergence to steady-states for trajectories of a damped dynamical system modeling adhesive strings

An interior penalty coupling strategy for isogeometric non-conformal Kirchhoff-Love shell patches

Model reduction of coupled systems based on non-intrusive approximations of the boundary response maps

Hitting with Probability One for Stochastic Heat Equations with Additive Noise

The Advection Boundary Law in presence of mean-flow and spinning modes

Parametric Reduced Order Model of a Gas Bearings Supported Rotor

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Graph generative deep learning models with an application to circuit topologies

Radiative transfer in luminescent solar concentrators

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

First-principle based predictions of the effects of negative triangularity on DTT scenarios

Robust machine learning for neuroscientific inference

Experimental Investigation on Size-Effect of Rubble Stone Masonry Walls Under In-Plane Horizontal Loading: Overview and Preliminary Results

Random matrix methods for high-dimensional machine learning models

RE:SLAB — a load bearing system for open-ended component reuse in building structures

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Machine Learning for Modeling Stock Returns