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Hybrid Machine Learning Algorithms f...

Pakravan, Samira,

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Hybrid Machine Learning Algorithms for Solving Forward and Inverse Problems in Physical Sciences /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Hybrid Machine Learning Algorithms for Solving Forward and Inverse Problems in Physical Sciences // Samira Pakravan.
作者:	Pakravan, Samira,
面頁冊數:	1 electronic resource (143 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
標題:	Mechanical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31332237
ISBN:	9798384033622

Hybrid Machine Learning Algorithms for Solving Forward and Inverse Problems in Physical Sciences /
Pakravan, Samira,

Hybrid Machine Learning Algorithms for Solving Forward and Inverse Problems in Physical Sciences /Samira Pakravan. - 1 electronic resource (143 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

Machine learning (ML) techniques have emerged as powerful tools for solving differential equations, particularly in the context of partial differential equations (PDEs), enabling accelerated forward simulations and parameter discovery from limited data. However, challenges persist in maintaining numerical accuracy, especially in scenarios requiring real-time inference and inverse problem-solving. This dissertation investigates innovative hybrid strategies that blend classical finite discretization methods with modern ML techniques to enhance accuracy while maintaining computational efficiency. Our research focuses on addressing key challenges at the intersection of scientific computing, machine learning, and applied mathematics, including performance, accuracy, and data efficiency.

English

ISBN: 9798384033622Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Differentiable solvers

Hybrid Machine Learning Algorithms for Solving Forward and Inverse Problems in Physical Sciences /
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245 1 0 $a Hybrid Machine Learning Algorithms for Solving Forward and Inverse Problems in Physical Sciences / $c Samira Pakravan.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (143 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
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500 $a Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
500 $a Advisors: Gibou, Frederic G. Committee members: Pollock, Tresa M.; Yeung, Enoch; Luzzatto-Fegiz, Paolo.
502 $b Ph.D. $c University of California, Santa Barbara $d 2024.
520 $a Machine learning (ML) techniques have emerged as powerful tools for solving differential equations, particularly in the context of partial differential equations (PDEs), enabling accelerated forward simulations and parameter discovery from limited data. However, challenges persist in maintaining numerical accuracy, especially in scenarios requiring real-time inference and inverse problem-solving. This dissertation investigates innovative hybrid strategies that blend classical finite discretization methods with modern ML techniques to enhance accuracy while maintaining computational efficiency. Our research focuses on addressing key challenges at the intersection of scientific computing, machine learning, and applied mathematics, including performance, accuracy, and data efficiency.
546 $a English
590 $a School code: 0035
650 4 $a Mechanical engineering. $3 649730
650 4 $a Computational physics. $3 3343998
653 $a Differentiable solvers
653 $a Finite discretization methods
653 $a Physics-aware neural networks
653 $a Machine learning
653 $a Surrogate models
690 $a 0548
690 $a 0800
690 $a 0216
710 2 $a University of California, Santa Barbara. $b Mechanical Engineering. $e degree granting institution. $3 3829968
720 1 $a Gibou, Frederic G. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-03B.
790 $a 0035
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31332237