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Rigorously Quantifying Uncertainties...

Li, Yuanhao.

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Rigorously Quantifying Uncertainties for Transport Phenomena in Molecular Simulations.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Rigorously Quantifying Uncertainties for Transport Phenomena in Molecular Simulations./
作者:	Li, Yuanhao.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	122 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Contained By:	Dissertations Abstracts International85-04B.
標題:	Engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30688305
ISBN:	9798380478304

Rigorously Quantifying Uncertainties for Transport Phenomena in Molecular Simulations.
Li, Yuanhao.

Rigorously Quantifying Uncertainties for Transport Phenomena in Molecular Simulations. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 122 p.

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2023.

The field of computational materials science faces various challenges in data processing, including dealing with high-dimensional parameter spaces and error analysis. Uncertainty quantification has become crucial for interpreting the results of materials simulations. We begin by discussing the fundamentals and challenges associated with addressing uncertainties in molecular-dynamics (MD) simulations. Subsequently, we address two problems at the heart of uncertainty analysis for MD simulations. In the first problem, under the assumption that we have a large dataset consisting of numerous statistically independent MD datasets (each of which can be used to estimate a quantity of engineering interest via, e.g., regression analysis), we investigate the statistical confidence we can build using the large dataset as a whole. In the second problem, we study physical settings in which the assumption underlying the first problem is likely to fail, namely, settings in which nominally independent MD datasets are not in fact independent. Both problems have significant relevance for the interpretation of simulations of many nanoscale phenomena (e.g., confined fluid diffusion or heat transfer at fluid-solid interfaces).We discuss an approach for conducting regression analysis on time series data designed to circumvent the challenges posed by the first problem. Additionally, we explore an approach rooted in thermodynamic principles that accelerates the decorrelation between consecutive MD measurements, offering a solution to the second problem. Leveraging these approaches, we study thermal transport properties at a fluid-solid interface. Finally, we propose a Heteroscedastic Gaussian Process Regression workflow to model fluid self-diffusion coefficient as a function of thermodynamic conditions.

ISBN: 9798380478304Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Molecular-dynamics

Rigorously Quantifying Uncertainties for Transport Phenomena in Molecular Simulations.
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