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Applications of Physics-Informed Mac...

Ansari, Mehrad.

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Applications of Physics-Informed Machine Learning in Chemical Engineering.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Applications of Physics-Informed Machine Learning in Chemical Engineering./
作者:	Ansari, Mehrad.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	159 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
Contained By:	Dissertations Abstracts International85-03B.
標題:	Chemical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30529373
ISBN:	9798380314978

Applications of Physics-Informed Machine Learning in Chemical Engineering.
Ansari, Mehrad.

Applications of Physics-Informed Machine Learning in Chemical Engineering. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 159 p.

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

Thesis (Ph.D.)--University of Rochester, 2023.

This item must not be sold to any third party vendors.

Traditionally, testing of a given scientific hypothesis was hard due to the scarcity and limitations of the data that each experiment could produce. Today, experimental data collection at higher quantities, yet at lower relative costs has given a rise to data-driven modeling approaches. The main shortcoming of these black-box data-driven models is their complicated interpretability, mainly due to lacking a connection to the system's underlying physics. In contrast, physics-based models try to generate predictive data by solving complex mathematical formulations that are derived based on the conservation laws and physical principles. Regardless of their complexity, these models are still a proxy representation of a biased physical reality and in many cases, they fail at replicating the natural phenomena to the full extent. In this dissertation, the two approaches are combined over a variety of different problems relevant to chemical engineering, with the key idea of mitigating the disadvantages of using purely physics-based or data-driven models. This research demonstrates on flexibility of machine learning in application to computational fluid dynamics, epidemiological modeling, and peptide design.

ISBN: 9798380314978Subjects--Topical Terms:

560457
Chemical engineering.
Subjects--Index Terms:

Cheminformatics

Applications of Physics-Informed Machine Learning in Chemical Engineering.
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500 $a Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
500 $a Advisor: White, Andrew D.
502 $a Thesis (Ph.D.)--University of Rochester, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Traditionally, testing of a given scientific hypothesis was hard due to the scarcity and limitations of the data that each experiment could produce. Today, experimental data collection at higher quantities, yet at lower relative costs has given a rise to data-driven modeling approaches. The main shortcoming of these black-box data-driven models is their complicated interpretability, mainly due to lacking a connection to the system's underlying physics. In contrast, physics-based models try to generate predictive data by solving complex mathematical formulations that are derived based on the conservation laws and physical principles. Regardless of their complexity, these models are still a proxy representation of a biased physical reality and in many cases, they fail at replicating the natural phenomena to the full extent. In this dissertation, the two approaches are combined over a variety of different problems relevant to chemical engineering, with the key idea of mitigating the disadvantages of using purely physics-based or data-driven models. This research demonstrates on flexibility of machine learning in application to computational fluid dynamics, epidemiological modeling, and peptide design.
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653 $a Machine learning
653 $a Peptide design
653 $a Simulation-based Inference
653 $a Physical principles
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690 $a 0204
710 2 $a University of Rochester. $b Hajim School of Engineering and Applied Sciences. $3 2099687
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