Bayesian uncertainty management in temporal dependence of extremes

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.

For the dimensioning and assessment of structures, it is common practice to compare action effects with sectional resistances. Extensive studies have been performed to quantify the model uncertainty on the resistance side. However, for statically indetermi ...

Recently, there has been an arms race of pose forecasting methods aimed at solving the spatio-temporal task of predicting a sequence of future 3D poses of a person given a sequence of past observed ones. However, the lack of unified benchmarks and limited ...

Whereas the ability of deep networks to produce useful predictions on many kinds of data has been amply demonstrated, estimating the reliability of these predictions remains challenging. Sampling approaches such as MC-Dropout and Deep Ensembles have emerge ...

As a follow-up to the CEA-EPFL PETALE experimental program on stainless steel nuclear data, the EPFL initiated an open-loop pile-oscillation experimental program in the CROCUS reactor: BLOOM. A reproduction of the critical experiments of PETALE, the progra ...

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

This thesis presents work at the junction of statistics and climate science. We first provide methodology for use by climate scientists when performing fast event attribution using extreme value theory, and then describe two interdisciplinary projects in c ...

Aims: Understanding the correlations between underlying medical and personal characteristics of a patient with cancer and the risk of lung metastasis may improve clinical management and outcomes. We used machine learning methodologies to predict the risk o ...

Buildings are a promising source of flexibility for the application of demand response. In this work, we introduce a novel battery model formulation to capture the state evolution of a single building. Being fully data-driven, the battery model identificat ...

This study compares three imputation methods applied to the field observations of hydraulic head in subsurface hydrology. Hydrogeological studies that analyze the timeseries of groundwater elevations often face issues with missing data that may mislead bot ...

Every engineering calculation is an approximation of reality, with inevitable uncertainties involved. This fact implies that a reliability verification accounting for the uncertainties is a necessary step in the design and assessment of structures. Nowaday ...

Aerosol forcing uncertainty represents the largest climate forcing uncertainty overall. Its magnitude has remained virtually undiminished over the past 20 years despite considerable advances in understanding most of the key contributing elements. Recent wo ...

A novel approach for robust controller synthesis, which models uncertainty as an elliptical set, is proposed in the paper. Given a set of frequency response functions of linear time-invariant (LTI) multiple-input multiple-output (MIMO) systems, the approac ...

Advanced momentum transport analysis is used to study matched hydrogen (H) and deuterium (D) plasmas in the core of ASDEX Upgrade. The aim is to validate gyrokinetic theory and assess a possible isotope dependence. The methodology extracts momentum diffusi ...

We present a control framework for achieving a robust object grasp and manipulation in hand. In-hand manipulation remains a demanding task as the object is never stable and task success relies on carefully synchronizing the fingers' dynamics. Indeed, finge ...

Service-level requirements play a crucial role in eliminating stock-outs in a production pipeline. However, delivering a specific service level can become an unattainable goal given the various uncertainties influencing both the production pipeline and cus ...

The quantification of uncertainties can be particularly challenging for problems requiring long-time integration as the structure of the random solution might considerably change over time. In this respect, dynamical low-rank approximation (DLRA) is very a ...

We provide a computationally and statistically efficient method for estimating the parameters of a stochastic covariance model observed on a regular spatial grid in any number of dimensions. Our proposed method, which we call the Debiased Spatial Whittle l ...

In superstructure optimization of processes and energy systems, the design space is defined as the combination of unit considerations, process conditions and model parameters that might be subjected to uncertainty. Most of the time, decision makers are not ...

An analytical expression to estimate the uncertainty on the frequency shift in ϕ-OTDR systems is established and confirmed by actual data. ...

This thesis focuses on non-parametric covariance estimation for random surfaces, i.e.~functional data on a two-dimensional domain. Non-parametric covariance estimation lies at the heart of functional data analysis, and considerations of statistical and com ...

Bayesian uncertainty management in temporal dependence of extremes

Chattez avec Graph Search

Model uncertainties in action effects and load bearing capacity calculation in statically indeterminate reinforced concrete structures

Toward Reliable Human Pose Forecasting With Uncertainty

ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference

Design of an Open-Loop Pile-Oscillation Program in the CROCUS Reactor

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Wind, Hail, and Climate Extremes: Modelling and Attribution Studies for Environmental Data

Medical and Personal Characteristics Can Predict the Risk of Lung Metastasis

Uncertainty-aware Flexibility Envelope Prediction in Buildings with Controller-agnostic Battery Models

Comparison of Three Imputation Methods for Groundwater Level Timeseries

Reliability analysis and partial safety format calibration considering the characteristics of the resistance of reinforced concrete structures

Reducing Aerosol Forcing Uncertainty by Combining Models With Satellite and Within-The-Atmosphere Observations: A Three-Way Street

Data-driven IQC-Based Robust Control Design for Hybrid Micro-disturbance Isolation Platform

Comparison of momentum transport in matched hydrogen and deuterium H-mode plasmas in ASDEX Upgrade

Adaptive Fingers Coordination for Robust Grasp and In-Hand Manipulation Under Disturbances and Unknown Dynamics

Limiting the impact of supply chain disruptions in the face of distributional uncertainty in demand

Dynamical low rank approximation for uncertainty quantification of time-dependent problems

The Debiased Spatial Whittle likelihood

Active learning for multi-objective optimization of processes and energy systems

Analytical prediction of the final error due to noise in a ϕ-OTDR system

Covariance Estimation for Random Surfaces beyond Separability