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You searched for +publisher:"Brown University" +contributor:("Venturi, Daniele"). Showing records 1 – 2 of 2 total matches.

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1. Perdikaris, Paris. Data-Driven Parallel Scientific Computing: Multi-Fidelity Information Fusion Algorithms and Applications to Physical and Biological Systems.

Degree: PhD, Applied Mathematics, 2015, Brown University

This thesis takes aim at two directions. The first direction involves setting the foundations for a new type of data-driven scientific computing, essentially creating a platform for blending multiple information sources of variable fidelity, e.g., experimental data, high-fidelity numerical simulations, expert opinion, etc., towards creating tractable paths to analyzing the response of complex physical and biological systems. Standing on the verge of traditional scientific computing and contemporary statistical learning techniques, we elaborate on a novel multi-fidelity information fusion framework that allows for the seamless integration of surrogate-based optimization and uncertainty quantification, and enables the development of efficient algorithms for data assimilation, design optimization, model inversion, and beyond. The second direction is focused on addressing open questions in the modeling of the human circulatory system, especially the interplay between blood flow and biomechanics in the brain Despite great growth in computing power and algorithmic sophistication, in-silico modeling of blood flow and arterial mechanics is still limited to truncated arterial domains, a fact that introduces the need for reduced-order models and parametric representations to account for the neglected mesoscale dynamics. The implicit need of such simplified representations gives rise to a series of open questions in cardiovascular mathematics, three of which will be at the focal point of our attention. To this end, we will introduce robust fractional-order constitutive laws for arterial biomechanics, we will address the closure problem for hemodynamic simulations in truncated arterial domains by coupling terminal outlet vessels to nonlinear 1D blood flow models in fractal arterial trees, and we will propose a model inversion technique via multi-fidelity surrogates that enables the efficient solution of parameter calibration problems. Advisors/Committee Members: Karniadakis, George (Director), Royset, Johannes (Reader), Venturi, Daniele (Reader).

Subjects/Keywords: Multi-fidelity modeling

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Perdikaris, P. (2015). Data-Driven Parallel Scientific Computing: Multi-Fidelity Information Fusion Algorithms and Applications to Physical and Biological Systems. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:419448/

Chicago Manual of Style (16th Edition):

Perdikaris, Paris. “Data-Driven Parallel Scientific Computing: Multi-Fidelity Information Fusion Algorithms and Applications to Physical and Biological Systems.” 2015. Doctoral Dissertation, Brown University. Accessed January 16, 2021. https://repository.library.brown.edu/studio/item/bdr:419448/.

MLA Handbook (7th Edition):

Perdikaris, Paris. “Data-Driven Parallel Scientific Computing: Multi-Fidelity Information Fusion Algorithms and Applications to Physical and Biological Systems.” 2015. Web. 16 Jan 2021.

Vancouver:

Perdikaris P. Data-Driven Parallel Scientific Computing: Multi-Fidelity Information Fusion Algorithms and Applications to Physical and Biological Systems. [Internet] [Doctoral dissertation]. Brown University; 2015. [cited 2021 Jan 16]. Available from: https://repository.library.brown.edu/studio/item/bdr:419448/.

Council of Science Editors:

Perdikaris P. Data-Driven Parallel Scientific Computing: Multi-Fidelity Information Fusion Algorithms and Applications to Physical and Biological Systems. [Doctoral Dissertation]. Brown University; 2015. Available from: https://repository.library.brown.edu/studio/item/bdr:419448/

2. Cho, Heyrim. High-Dimensional Response-Excitation PDF Methods for Uncertainty Quantification and Stochastic Modeling.

Degree: PhD, Applied Mathematics, 2015, Brown University

The probability density approach based on the response-excitation theory is developed for stochastic simulations of non-Markovian systems. This approach provides the complete probabilistic configuration of the solution that enables a comprehensive study of stochastic systems. By using functional integral methods we determine a computable evolution equation for the joint response-excitation probability density function (REPDF) of stochastic dynamical systems and stochastic partial differential equations driven by colored noise. We establish its connection to the classical response approach and its agreement to the Dostupov-Pugachev equations (Dostupov, 1957) and the Malakhov-Saichev equations (Gurbatov et al, 1991). An efficient algorithm has been proposed by using adaptive discontinuous Galerkin method and probabilistic collocation method combined with sparse grid. For high-dimensional REPDF systems, we develop the algorithms concerning high-dimensional numerical approximations, namely, separated series expansion and the ANOVA approximation. These methods reduce the computational cost in high-dimensions to several low-dimensional operations. Alternatively, reduced order PDF equations are obtained by using the Mori-Zwanzig framework and conditional moment closures, which establish a preliminary work of goal-oriented PDF equations. Finally, we demonstrate the effectiveness of the proposed numerical methods to various stochastic systems including the tumor cell growth model, chaotic nonlinear oscillators, advection reaction equation, and Burgers equation. The second part of the thesis focuses on simulations of multi-scale stochastic systems. The Karhunen-Loeve expansion is extended to characterize multiple correlated random processes and local decomposed random fields. We then propose interface conditions based on conditional moments and PDE-constrained optimization that preserve the global statistics while propagating uncertainty. Finally, the decomposition algorithm is recast to couple distinct PDF models including the REPDF system. Advisors/Committee Members: Karniadakis, George (Director), Rozovsky, Boris (Reader), Venturi, Daniele (Reader), Sapsis, Themistoklis (Reader).

Subjects/Keywords: Uncertainty quantification

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Cho, H. (2015). High-Dimensional Response-Excitation PDF Methods for Uncertainty Quantification and Stochastic Modeling. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:419509/

Chicago Manual of Style (16th Edition):

Cho, Heyrim. “High-Dimensional Response-Excitation PDF Methods for Uncertainty Quantification and Stochastic Modeling.” 2015. Doctoral Dissertation, Brown University. Accessed January 16, 2021. https://repository.library.brown.edu/studio/item/bdr:419509/.

MLA Handbook (7th Edition):

Cho, Heyrim. “High-Dimensional Response-Excitation PDF Methods for Uncertainty Quantification and Stochastic Modeling.” 2015. Web. 16 Jan 2021.

Vancouver:

Cho H. High-Dimensional Response-Excitation PDF Methods for Uncertainty Quantification and Stochastic Modeling. [Internet] [Doctoral dissertation]. Brown University; 2015. [cited 2021 Jan 16]. Available from: https://repository.library.brown.edu/studio/item/bdr:419509/.

Council of Science Editors:

Cho H. High-Dimensional Response-Excitation PDF Methods for Uncertainty Quantification and Stochastic Modeling. [Doctoral Dissertation]. Brown University; 2015. Available from: https://repository.library.brown.edu/studio/item/bdr:419509/

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