Propagating uncertainties in large-scale hemodynamics models via network uncertainty quantification and reduced-order modeling
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This paper proposes to integrate the Transverse Enriched Pipe Element Methods as a reduced-order model for effectively computing the 3D local hemodynamics and a combination of uncertainty quantification via Polynomial Chaos Expansion and classical relaxation methods for effectively propagating random variables that encode uncertainties throughout the networks.About:
This article is published in Computer Methods in Applied Mechanics and Engineering.The article was published on 2020-01-01 and is currently open access. It has received 24 citations till now. The article focuses on the topics: Uncertainty quantification & Polynomial chaos.read more
Citations
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Data-driven recovery of hidden physics in reduced order modeling of fluid flows
TL;DR: In this paper, a modular hybrid analysis and modeling (HAM) approach is proposed to account for hidden physics in reduced order modeling of parameterized systems relevant to fluid dynamics, which employs proper orthogonal decomposition as a compression tool to construct orthonormal bases and a Galerkin projection (GP) as a model to build the dynamical core of the system.
Journal ArticleDOI
Data-driven recovery of hidden physics in reduced order modeling of fluid flows
TL;DR: A modular hybrid analysis and modeling approach to account for hidden physics in reduced order modeling of parameterized systems relevant to fluid dynamics provides insights addressing a fundamental limitation of the physics-based models when the governing equations are incomplete to represent underlying physical processes.
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Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics.
TL;DR: In this article, a multilevel multifidelity Monte Carlo (MLMF) estimator was proposed to improve the accuracy of hemodynamic quantities of interest while maintaining reasonable computational cost.
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Modeling Water Quality in Watersheds: From Here to the Next Generation
Baihua Fu,Jeffery S. Horsburgh,Anthony Jakeman,Carlo Gualtieri,T. Arnold,Lucy Marshall,Timothy R. Green,Nigel W.T. Quinn,Martin Volk,Randall J. Hunt,L. Vezzaro,Barry Croke,John D. Jakeman,Val Snow,B. Rashleigh +14 more
TL;DR: This synthesis makes three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices, including building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge.
Posted Content
Multiscale modeling meets machine learning: What can we learn?
Grace C.Y. Peng,Mark Alber,Adrian Buganza Tepole,William R. Cannon,Suvranu De,Salvador Dura-Bernal,Krishna Garikipati,George Em Karniadakis,William W. Lytton,Paris Perdikaris,Linda R. Petzold,Ellen Kuhl +11 more
TL;DR: In this paper, the authors identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscales modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function.
References
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The finite element method
TL;DR: In this article, the methodes are numeriques and the fonction de forme reference record created on 2005-11-18, modified on 2016-08-08.
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The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
TL;DR: This work represents the stochastic processes with an optimum trial basis from the Askey family of orthogonal polynomials that reduces the dimensionality of the system and leads to exponential convergence of the error.
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Blood flow in arteries
TL;DR: The study of arterial blood flow will lead to the prediction of individual hemodynamic flows in any patient, the development of diagnostic tools to quantify disease, and the design of devices that mimic or alter blood flow.