Efficient inversion and uncertainty quantification of a tephra fallout model: TEPHRA INVERSION
Sign Up to like & getrecommendations! 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
Efficient sampling for polynomial chaos-based uncertainty quantification and sensitivity analysis using weighted approximate Fekete points
Sign Up to like & getrecommendations! 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
Computational uncertainty quantification for random time-discrete epidemiological models using adaptive gPC
Sign Up to like & getrecommendations! 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
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 & getrecommendations! 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
Uncertainty quantification for multilabel text classification
Sign Up to like & getrecommendations! 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
Error-lumped inverse uncertainty quantification of automotive heat exchangers (HEXs) using large-scale database from system level tests
Sign Up to like & getrecommendations! 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
Engaging soft computing in material and modeling uncertainty quantification of dam engineering problems
Sign Up to like & getrecommendations! Published in 2020 at "Soft Computing"
DOI: 10.1007/s00500-019-04623-x
Abstract: Due to complex nature of nearly all infrastructures (and more specifically concrete dams), the uncertainty quantification is an inseparable part of risk assessment. Uncertainties might be propagated in different aspects depending on their relative importance… read more here.
Keywords: soft computing; material modeling; material; uncertainty quantification ... See more keywords
Sensitivity analysis and inverse uncertainty quantification for the left ventricular passive mechanics.
Sign Up to like & getrecommendations! Published in 2022 at "Biomechanics and modeling in mechanobiology"
DOI: 10.1007/s10237-022-01571-8
Abstract: Personalized computational cardiac models are considered to be a unique and powerful tool in modern cardiology, integrating the knowledge of physiology, pathology and fundamental laws of mechanics in one framework. They have the potential to… read more here.
Keywords: uncertainty; model; sensitivity; uncertainty quantification ... See more keywords
Uncertainty quantification for stochastic dynamical systems using time-dependent stochastic bases
Sign Up to like & getrecommendations! Published in 2018 at "Applied Mathematics and Mechanics"
DOI: 10.1007/s10483-019-2409-6
Abstract: A novel method based on time-dependent stochastic orthogonal bases for stochastic response surface approximation is proposed to overcome the problem of significant errors in the utilization of the generalized polynomial chaos (GPC) method that approximates… read more here.
Keywords: uncertainty quantification; dependent stochastic; method; stochastic bases ... See more keywords
Uncertainty Quantification in Atomistic Modeling of Metals and Its Effect on Mesoscale and Continuum Modeling: A Review
Sign Up to like & getrecommendations! Published in 2021 at "JOM"
DOI: 10.1007/s11837-020-04436-6
Abstract: The design of next-generation alloys through the integrated computational materials engineering (ICME) approach relies on multiscale computer simulations to provide thermodynamic properties when experiments are difficult to conduct. Atomistic methods such as density functional theory… read more here.
Keywords: mesoscale; uncertainty; uncertainty quantification; atomistic modeling ... See more keywords
Uncertainty Quantification of a Multiscale Model for In-Stent Restenosis
Sign Up to like & getrecommendations! Published in 2018 at "Cardiovascular Engineering and Technology"
DOI: 10.1007/s13239-018-00372-4
Abstract: PurposeCoronary artery stenosis, or abnormal narrowing, is a widespread and potentially fatal cardiac disease. After treatment by balloon angioplasty and stenting, restenosis may occur inside the stent due to excessive neointima formation. Simulations of in-stent… read more here.
Keywords: uncertainty quantification; model; restenosis; stent restenosis ... See more keywords