LAUSR

About LAUSR
Donate
Contact Us

Follow us:

You are not signed in:

Sign Up!


   Articles with "uncertainty quantification" as a keyword



Efficient inversion and uncertainty quantification of a tephra fallout model: TEPHRA INVERSION

Sign Up to like & get
recommendations! Published in 2017 at "Journal of Geophysical Research"

DOI: 10.1002/2016jb013682

Abstract: An efficient and effective inversion and uncertainty quantification approach is proposed for estimating eruption parameters given a data set collected from a tephra deposit. The approach is model independent and here is applied using Tephra2,… read more here.

Keywords: uncertainty; tephra; uncertainty quantification; eruption ... See more keywords
Photo from wikipedia

Efficient sampling for polynomial chaos-based uncertainty quantification and sensitivity analysis using weighted approximate Fekete points

Sign Up to like & get
recommendations! Published in 2020 at "International journal for numerical methods in biomedical engineering"

DOI: 10.1002/cnm.3395

Abstract: Performing uncertainty quantification (UQ) and sensitivity analysis (SA) is vital when developing a patient-specific physiological model because it can quantify model output uncertainty and estimate the effect of each of the model's input parameters on… read more here.

Keywords: uncertainty; uncertainty quantification; analysis; model ... See more keywords

Uncertainty Quantification in Flow Cytometry Using a Cell Sorter

Sign Up to like & get
recommendations! Published in 2024 at "Cytometry Part A"

DOI: 10.1002/cyto.a.24925

Abstract: In cytometry, it is difficult to disentangle the contributions of population variance and instrument noise toward total measured variation. Fundamentally, this is due to the fact that one cannot measure the same particle multiple times.… read more here.

Keywords: cytometry; cell sorter; quantification flow; uncertainty quantification ... See more keywords
Photo from academic.microsoft.com

Computational uncertainty quantification for random time-discrete epidemiological models using adaptive gPC

Sign Up to like & get
recommendations! Published in 2018 at "Mathematical Methods in the Applied Sciences"

DOI: 10.1002/mma.5315

Abstract: This work has been supported by the Spanish Ministerio de Economia y Competitividad grant MTM2017-89664-P. Marc Jornet acknowledges the doctorate scholarship granted by Programa de Ayudas de Investigacion y Desarrollo (PAID), Universitat Politecnica de Valencia.… read more here.

Keywords: quantification random; uncertainty quantification; discrete epidemiological; random time ... See more keywords

Uncertainty quantification in multi‐class segmentation: Comparison between Bayesian and non‐Bayesian approaches in a clinical perspective

Sign Up to like & get
recommendations! Published in 2024 at "Medical Physics"

DOI: 10.1002/mp.17189

Abstract: Automatic segmentation techniques based on Convolutional Neural Networks (CNNs) are widely adopted to automatically identify any structure of interest from a medical image, as they are not time consuming and not subject to high intra‐… read more here.

Keywords: segmentation; approaches clinical; quantification multi; uncertainty quantification ... See more keywords
Photo by sarahsosiak from unsplash

DeepCEST 3T: Robust MRI parameter determination and uncertainty quantification with neural networks—application to CEST imaging of the human brain at 3T

Sign Up to like & get
recommendations! Published in 2019 at "Magnetic Resonance in Medicine"

DOI: 10.1002/mrm.28117

Abstract: Calculation of sophisticated MR contrasts often requires complex mathematical modeling. Data evaluation is computationally expensive, vulnerable to artifacts, and often sensitive to fit algorithm parameters. In this work, we investigate whether neural networks can provide… read more here.

Keywords: uncertainty quantification; mri; deepcest robust; neural networks ... See more keywords

Forward and Inverse Approaches for Uncertainty Quantification of the In‐Plane Elastic Properties of Cellular Structures

Sign Up to like & get
recommendations! Published in 2025 at "International Journal for Numerical Methods in Engineering"

DOI: 10.1002/nme.70035

Abstract: Uncertainty quantification is essential to exploiting the complete potential of cellular structures. Forward methods allow quantifying the uncertainties in the mechanical properties of cellular structures by propagating the uncertainties of the input parameters, while inverse… read more here.

Keywords: cellular structures; plane elastic; uncertainty quantification; geometry ... See more keywords

Development of a Projection‐Based Advanced Deterministic Uncertainty Analysis in Low Dimensions

Sign Up to like & get
recommendations! Published in 2025 at "International Journal for Numerical Methods in Engineering"

DOI: 10.1002/nme.70156

Abstract: This paper provides an in‐depth exploration of reduced order modeling and uncertainty quantification relevant to engineering disciplines. The objective of this study is to develop an innovative uncertainty quantification algorithm incorporating a reduced order model… read more here.

Keywords: analysis; uncertainty quantification; uncertainty; deterministic uncertainty ... See more keywords

Uncertainty quantification for multilabel text classification

Sign Up to like & get
recommendations! Published in 2020 at "Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"

DOI: 10.1002/widm.1384

Abstract: Deep neural networks have recently achieved impressive performance on multilabel text classification. However, the uncertainty in multilabel text classification tasks and their application in the model are often overlooked. To better understand and evaluate the… read more here.

Keywords: text classification; classification; uncertainty; multilabel text ... See more keywords

Uncertainty quantification for μ → e conversion in nuclei: charge distributions

Sign Up to like & get
recommendations! Published in 2024 at "Journal of High Energy Physics"

DOI: 10.1007/jhep08(2024)052

Abstract: Predicting the rate for μ → e conversion in nuclei for a given set of effective operators mediating the violation of lepton flavor symmetry crucially depends on hadronic and nuclear matrix elements. In particular, the… read more here.

Keywords: quantification conversion; conversion; charge distributions; uncertainty quantification ... See more keywords

Error-lumped inverse uncertainty quantification of automotive heat exchangers (HEXs) using large-scale database from system level tests

Sign Up to like & get
recommendations! Published in 2021 at "Structural and Multidisciplinary Optimization"

DOI: 10.1007/s00158-021-02946-8

Abstract: Reliability-based design optimization (RBDO) utilizing computer simulations can lead to a highly reliable optimum design. However, conventional RBDO methods require full statistical information of input variables to estimate reliabilities of engineering systems or components, which… read more here.

Keywords: system; uncertainty quantification; error lumped; error ... See more keywords