![The impact of uncertainty on predictions of the CovidSim epidemiological code | Nature Computational Science The impact of uncertainty on predictions of the CovidSim epidemiological code | Nature Computational Science](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs43588-021-00028-9/MediaObjects/43588_2021_28_Fig1_HTML.png)
The impact of uncertainty on predictions of the CovidSim epidemiological code | Nature Computational Science
![Frontiers | Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience Frontiers | Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience](https://www.frontiersin.org/files/Articles/370145/fninf-12-00049-HTML/image_m/fninf-12-00049-g001.jpg)
Frontiers | Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience
![Integrated uncertainty quantification and sensitivity analysis of single-component dynamic column breakthrough experiments | SpringerLink Integrated uncertainty quantification and sensitivity analysis of single-component dynamic column breakthrough experiments | SpringerLink](https://media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10450-022-00361-z/MediaObjects/10450_2022_361_Fig3_HTML.png)
Integrated uncertainty quantification and sensitivity analysis of single-component dynamic column breakthrough experiments | SpringerLink
![PDF] Model uncertainty of interfacial area and mass transfer coefficients in absorption column packings | Semantic Scholar PDF] Model uncertainty of interfacial area and mass transfer coefficients in absorption column packings | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/9f70fec9196a2a0d949421be8a4f6736c716dcf3/4-Figure3-1.png)
PDF] Model uncertainty of interfacial area and mass transfer coefficients in absorption column packings | Semantic Scholar
Uncertainty Evaluation of the Diffusive Gradients in Thin Films Technique | Environmental Science & Technology
![Uncertainty quantification of granular computing-neural network model for prediction of pollutant longitudinal dispersion coefficient in aquatic streams | Scientific Reports Uncertainty quantification of granular computing-neural network model for prediction of pollutant longitudinal dispersion coefficient in aquatic streams | Scientific Reports](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41598-022-08417-4/MediaObjects/41598_2022_8417_Fig1_HTML.png)
Uncertainty quantification of granular computing-neural network model for prediction of pollutant longitudinal dispersion coefficient in aquatic streams | Scientific Reports
![TC - Inferring the basal sliding coefficient field for the Stokes ice sheet model under rheological uncertainty TC - Inferring the basal sliding coefficient field for the Stokes ice sheet model under rheological uncertainty](https://tc.copernicus.org/articles/15/1731/2021/tc-15-1731-2021-avatar-web.png)
TC - Inferring the basal sliding coefficient field for the Stokes ice sheet model under rheological uncertainty
![A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches - ScienceDirect A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches - ScienceDirect](https://ars.els-cdn.com/content/image/1-s2.0-S0043135422013677-ga1.jpg)
A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches - ScienceDirect
![Applied Sciences | Free Full-Text | Uncertainty Quantification for Numerical Solutions of the Nonlinear Partial Differential Equations by Using the Multi-Fidelity Monte Carlo Method Applied Sciences | Free Full-Text | Uncertainty Quantification for Numerical Solutions of the Nonlinear Partial Differential Equations by Using the Multi-Fidelity Monte Carlo Method](https://pub.mdpi-res.com/applsci/applsci-12-07045/article_deploy/html/images/applsci-12-07045-g001.png?1657785857)
Applied Sciences | Free Full-Text | Uncertainty Quantification for Numerical Solutions of the Nonlinear Partial Differential Equations by Using the Multi-Fidelity Monte Carlo Method
![Applied Sciences | Free Full-Text | Uncertainty Assessment for Determining the Discharge Coefficient C for a Multi-Opening Orifice Applied Sciences | Free Full-Text | Uncertainty Assessment for Determining the Discharge Coefficient C for a Multi-Opening Orifice](https://pub.mdpi-res.com/applsci/applsci-10-08503/article_deploy/html/images/applsci-10-08503-g001.png?1606557501)
Applied Sciences | Free Full-Text | Uncertainty Assessment for Determining the Discharge Coefficient C for a Multi-Opening Orifice
![SOLVED: Heat transfer Step by step please Error Propagation-Uncertainty analysis -Review Determine the uncertainty in the heat transfer coefficient [Sh/h] calculation when using the following apparatus below and the equation: Q=hA*Tsurface-T air) SOLVED: Heat transfer Step by step please Error Propagation-Uncertainty analysis -Review Determine the uncertainty in the heat transfer coefficient [Sh/h] calculation when using the following apparatus below and the equation: Q=hA*Tsurface-T air)](https://cdn.numerade.com/ask_images/1e45bc2f1dcd46efad21f6e9a9a2a995.jpg)
SOLVED: Heat transfer Step by step please Error Propagation-Uncertainty analysis -Review Determine the uncertainty in the heat transfer coefficient [Sh/h] calculation when using the following apparatus below and the equation: Q=hA*Tsurface-T air)
![Model Parameter Estimation and Uncertainty Analysis: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group–6 - Andrew H. Briggs, Milton C. Weinstein, Elisabeth A. L. Fenwick, Jonathan Karnon, Model Parameter Estimation and Uncertainty Analysis: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group–6 - Andrew H. Briggs, Milton C. Weinstein, Elisabeth A. L. Fenwick, Jonathan Karnon,](https://journals.sagepub.com/cms/10.1177/0272989X12458348/asset/images/large/10.1177_0272989x12458348-fig1.jpeg)