Quantifying the Effects of Modeling Simplifications for Structural Identification of Bridges

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

Battery health prediction is significant while challenging for intelligent battery management. This article proposes a general framework for both short-term and long-term predictions of battery health under unseen dynamic loading and temperature conditions ...

We perform an error analysis of a fully discretised Streamline Upwind Petrov Galerkin Dynamical Low Rank (SUPG-DLR) method for random time-dependent advection-dominated problems. The time integration scheme has a splitting-like nature, allowing for potenti ...

Electrochemical Impedance Spectroscopy (EIS) and Equivalent Circuit Models (ECMs) are widely used to characterize the impedance and estimate parameters of electrochemical systems such as batteries. We use a generic ECM with ten parameters grouped to model ...

The goal of this work is to use anisotropic adaptive finite elements for the numerical simulation of aluminium electrolysis. The anisotropic adaptive criteria are based on a posteriori error estimates derived for simplified problems. First, we consider an ...

Deep heteroscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood. However, recent works show that this may result in sub-optimal convergence due to the challenges associated ...

There is a bias in the inference pipeline of most diffusion models. This bias arises from a signal leak whose distribution deviates from the noise distribution, creating a discrepancy between training and inference processes. We demonstrate that this signa ...

In certain cases of astronomical data analysis, the meaningful physical quantity to extract is the ratio R between two data sets. Examples include the lensing ratio, the interloper rate in spectroscopic redshift samples, and the decay rate of gravitational ...

In this article, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained fo ...

Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It involves controlling a system with linear dynamics and imperfect observations ...

Correlated errors of experimental data are a common but often neglected problem in physical sciences. Various tools are provided here for thorough propagation of uncertainties in cases of correlated errors. Discussed are techniques especially applicable to ...

This paper proposes a novel method to improve georeferencing of airborne laser scanning by improved trajectory estimation using Vehicle Dynamic Model. In Vehicle Dynamic Model (VDM), the relationship between the dynamics of the platform and control inputs ...

In the Large Hadron Collider, the beam losses are continuously measured for machine protection. By design, most of the particle losses occur in the collimation system, where the particles with high oscilla-tion amplitudes or large momentum error are scrape ...

Human thermo-physiology models (HTPM) are useful tools to assess dynamic and non-uniform human thermal states. However, they are developed based on the physiological data of an average person. In this paper, we present a detailed evaluation of two sophisti ...

Decisions regarding the management of civil infrastructure are becoming more crucial as a large share of bridges is presently approaching what is often considered to be the end of their theoretical service duration. Evaluating existing structures using a d ...

Managing existing civil infrastructure is challenging due to evolving functional requirements, material aging, and climate change. With increasingly limited economic, environmental, and material resources, more sustainable solutions for practical asset man ...

A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...

This work aims to study the effects of wind uncertainties in civil engineering structural design. Optimising the design of a structure for safety or operability without factoring in these uncertainties can result in a design that is not robust to these per ...

This paper presents our approach to predicting future error-related events in a robot-mediated gamified phys- ical training activity for stroke patients. The ability to predict future error under such conditions suggests the existence of distinguishable fe ...

Safety-critical navigation applications require that estimation errors be reliably quantified and bounded. Over the last decade, significant effort has been put to guarantee a bounded position estimation by using Global Navigation Satellite Systems (GNSS) ...

Quantifying the Effects of Modeling Simplifications for Structural Identification of Bridges

Chattez avec Graph Search

Spectral Estimators for High-Dimensional Matrix Inference

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

Error estimates for SUPG-stabilised Dynamical Low Rank Approximations

On the use of Cramér-Rao Lower Bound for least-variance circuit parameters identification of Li-ion cells

Adaptive Finite Elements with Large Aspect Ratio. Application to Aluminium Electrolysis

TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression

Exploiting the Signal-Leak Bias in Diffusion Models

An Unbiased Method of Measuring the Ratio of Two Data Sets

Quantization for Decentralized Learning Under Subspace Constraints

Distributionally Robust Linear Quadratic Control

Reliable uncertainties: Error correlation, rotated error bars, and linear regressions in three-isotope plots and beyond

Model-based constraints for trajectory determination of quad-copters: Design, calibration & merits for direct orientation

Data-driven modeling of beam loss in the LHC

Modeling local thermal responses of individuals: Validation of advanced human thermo-physiology models

Selection of informative monitoring techniques for bridge-performance evaluations

Sensing the structural behavior: A perspective on the usefulness of monitoring information for bridge examination

Multi-agent Learning with Privacy Guarantees

Multi-Level Monte Carlo Methods for Uncertainty Quantification and Risk-Averse Optimisation

Predictive Analysis of Errors During Robot-Mediated Gamified Training

GNSS/INS Kalman Filter Integrity Monitoring with Uncertain Time Correlated Error Processes