Uncertainty Quantification: Advances in Research and Applications

Luis Chase (Editor)

Series: Mathematics Research Developments
BISAC: MAT003000

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Volume 10

Issue 1

Volume 2

Volume 3

Special issue: Resilience in breaking the cycle of children’s environmental health disparities
Edited by I Leslie Rubin, Robert J Geller, Abby Mutic, Benjamin A Gitterman, Nathan Mutic, Wayne Garfinkel, Claire D Coles, Kurt Martinuzzi, and Joav Merrick

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In recent times, polynomial chaos expansion has emerged as a dominant technique to determine the response uncertainties of a system by propagating the uncertainties of the inputs. In this regard, the opening chapter of Uncertainty Quantification: Advances in Research and Applications, an intrusive approach called Galerkin Projection as well as non-intrusive approaches (such as pseudo-spectral projection and linear regression) are discussed.
Next, the authors introduce a new methodology to determine the uncertainties of input parameters using CIRCÉ software to overcome the reliance on expert judgment. The goal is to determinate and evaluate the uncertainty bounds for physical models related to reflood model of MARS-KS code Vessel module (coupled with COBRA-TF) using both CIRCÉ and the experimental data of FEBA.
Lastly, uncertainties related to rheological model parameters of skeletal muscles are modeled and analyzed, and available data are acquired and fused for hyperelastic constitutive model parameters with Neo-Hookean and Mooney-Rivlin formulations.
(Imprint: Nova)

Preface

Chapter 1. Polynomial Chaos for Uncertainty Quantification: Past, Present, and Future
(Mishal Thapa, Sameer B. Mulani and Robert W. Walters, Department of Aerospace Engineering and Mechanics, University of Alabama, Tuscaloosa, Alabama, US, and others)

Chapter 2. Uncertainty Evaluation for the Physical Models in MARS-KS 1.3 Based on FEBA Experimental Data
(Thanh Tram Tran, Bub Dong Chung and Hyun Sik Park, Korea University of Science and Technology, Gajeong-ro Yuseong-gu, Daejeon, Korea, and others)

Chapter 3. Uncertainty Modeling for Passive Skeletal Muscle Modeling Using Precise and Imprecise Probabilities
(Tien Tuan Dao, Sorbonne University, Université de Technologie de Compiègne, CNRS, UMR 7338 Biomechanics and Bioengineering, Compiègne, France, and others)

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