Results 41 to 50 of about 78,109 (159)

Uncertainty Quantification in Chemical Modeling

Eurasian Chemico-Technological Journal, 2018
A module of PrIMe automated data-centric infrastructure, Bound-to-Bound Data Collaboration (B2BDC), was used for the analysis of systematic uncertainty and data consistency of the H2/CO reaction model (73/17).
A. Mirzayeva, N. A. Slavinskaya, M. Abbasi, J. H. Starcke, W. Li, M. Frenklach   +5 more
doaj   +1 more source

Quantification of emission factor uncertainty [PDF]

Journal of the Air & Waste Management Association, 2012
Emissions factors are important for estimating and characterizing emissions from sources of air pollution. There is no quantitative indication of uncertainty for these emission factors, most factors do not have an adequate data set to compute uncertainty, and it is very difficult to locate the data for those that do.
George, Pouliot, Emily, Wisner, David, Mobley, William, Hunt   +3 more
openaire   +2 more sources

Improving the Quasi‐Biennial Oscillation via a Surrogate‐Accelerated Multi‐Objective Optimization

Journal of Advances in Modeling Earth Systems
Accurate simulation of the quasi‐biennial oscillation (QBO) is challenging due to uncertainties in representing convectively generated gravity waves.
Luis Damiano, Walter Hannah, Chih‐Chieh Chen, James J. Benedict, Khachik Sargsyan, Bert J. Debusschere, Michael S. Eldred   +6 more
doaj   +1 more source

A Deep Learning-Based Framework for Uncertainty Quantification in Medical Imaging Using the DropWeak Technique: An Empirical Study with Baresnet

Diagnostics, 2023
Lung cancer is a leading cause of cancer-related deaths globally. Early detection is crucial for improving patient survival rates. Deep learning (DL) has shown promise in the medical field, but its accuracy must be evaluated, particularly in the context ...
Mehmet Akif Cifci
doaj   +1 more source

Strategies for robust renovation of residential buildings in Switzerland

Nature Communications
Building renovation is urgently required to reduce the environmental impact associated with the building stock. Typically, building renovation is performed by envelope insulation and/or changing the fossil-based heating system.
Alina Galimshina, Maliki Moustapha, Alexander Hollberg, Sébastien Lasvaux, Bruno Sudret, Guillaume Habert   +5 more
doaj   +1 more source

Non-Probabilistic Uncertainty Quantification of Fiber-Reinforced Composite Laminate Based on Micro- and Macro-Mechanical Analysis

Applied Sciences, 2022
In this paper, the main aim is to study and predict macro elastic mechanical parameters of fiber-reinforced composite laminates by combining micro-mechanical analysis models and the non-probabilistic set theory.
Juxi Hu, Lei Wang, Xiaojun Wang
doaj   +1 more source

Strain tracking with uncertainty quantification

, 2023
AbstractThe ability to detect and quantify microbiota over time has a plethora of clinical, basic science, and public health applications. One of the primary means of tracking microbiota is through sequencing technologies. When the microorganism of interest is well characterized or knowna priori, targeted sequencing is often used. In many applications,
Younhun Kim, Colin J. Worby, Sawal Acharya, Lucas R. van Dijk, Daniel Alfonsetti, Zackary Gromko, Philippe Azimzadeh, Karen Dodson, Georg Gerber, Scott Hultgren, Ashlee M. Earl, Bonnie Berger, Travis E. Gibson   +12 more
openaire   +2 more sources

CLOSURE LAW MODEL UNCERTAINTY QUANTIFICATION

International Journal for Uncertainty Quantification, 2022
The prediction uncertainty in simulators for industrial processes is due to uncertainties in the input variables and uncertainties in specification of the models, in particular the closure laws. In this work, the uncertainty in each closure law was modeled as a random variable and the parameters of its distribution were optimized to correctly quantify ...
Strand, Andreas, Kjølaas, Jørn, Bergstrøm, Trond Harald, Steinsland, Ingelin, Hellevik, Leif Rune   +4 more
openaire   +2 more sources

Uncertainty Quantification For Learned ISTA

2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), 2023
to appear at the 33rd IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2023)
Hoppe, Frederik, Verdun, Claudio Mayrink, Krahmer, Felix, Laus, Hannah, Rauhut, Holger   +4 more
openaire   +2 more sources

Efficient Calculation of Uncertainty Quantification [PDF]

, 2014
We consider Uncertainty Quantification (UQ) by expanding the solution in so-called generalized Polynomial Chaos expansions. In these expansions the solution is decomposed into a series with orthogonal polynomials in which the parameter dependency becomes an argument of the orthogonal polynomial basis functions.
Maten, ter, E.J.W., Pulch, R., Schilders, W.H.A., Janssen, H.H.J.M.   +3 more
openaire   +2 more sources