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Enhancing Physical Modeling with Int...

Jacobsen, Christian S.,

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Enhancing Physical Modeling with Interpretable Physics-Aware Machine Learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Enhancing Physical Modeling with Interpretable Physics-Aware Machine Learning // Christian S Jacobsen.
Author:	Jacobsen, Christian S.,
Description:	1 electronic resource (251 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31349107
ISBN:	9798382741246

Enhancing Physical Modeling with Interpretable Physics-Aware Machine Learning /
Jacobsen, Christian S.,

Enhancing Physical Modeling with Interpretable Physics-Aware Machine Learning /Christian S Jacobsen. - 1 electronic resource (251 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

The burgeoning intersection of machine learning (ML) with physics has catalyzed a transformative approach to physical modeling marked by an enhanced capacity for innovation and discovery. Traditional applications of ML in physics often grapple with a critical challenge: predictions frequently lack transparency and interpretability, ultimately compromising generalizability and hindering the diagnosis of limitations. This opacity reduces the ability to develop generalized physical models and extract deeper insights into the underlying physical processes. This work aims to harness the capabilities of ML to learn from data while enhancing the interpretability of its models by creating physics-aware models. In doing so, it seeks to facilitate deeper analyses and enable more profound insights into physical phenomena, developing models which improve accuracy and generalizability over baseline methods. Through a series of studies, this work delves into the development and application of physically interpretable models in the realm of physics-aware machine learning. Each of the works, while distinct in their focus and application, collectively contributes to the advancement of interpretable physics-informed modeling with machine learning, addressing both theoretical and practical aspects. Together, these studies develop a narrative that underscores the importance of interpretability in machine learning for enhancing the generalizability of models. Additionally, it focuses on the development of physics-aware machine learning models to achieve this objective. Each piece of research, distinct in its application, contributes to a unified theme of enhancing the interpretability, generalizability, and practical utility of machine learning in the realm of physics. This body of work advances our understanding of how machine learning can be effectively applied to physics problems and offers new perspectives and methodologies that can aid in many aspects of scientific exploration and discovery.

English

ISBN: 9798382741246Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Computational science

Enhancing Physical Modeling with Interpretable Physics-Aware Machine Learning /
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520 $a The burgeoning intersection of machine learning (ML) with physics has catalyzed a transformative approach to physical modeling marked by an enhanced capacity for innovation and discovery. Traditional applications of ML in physics often grapple with a critical challenge: predictions frequently lack transparency and interpretability, ultimately compromising generalizability and hindering the diagnosis of limitations. This opacity reduces the ability to develop generalized physical models and extract deeper insights into the underlying physical processes. This work aims to harness the capabilities of ML to learn from data while enhancing the interpretability of its models by creating physics-aware models. In doing so, it seeks to facilitate deeper analyses and enable more profound insights into physical phenomena, developing models which improve accuracy and generalizability over baseline methods. Through a series of studies, this work delves into the development and application of physically interpretable models in the realm of physics-aware machine learning. Each of the works, while distinct in their focus and application, collectively contributes to the advancement of interpretable physics-informed modeling with machine learning, addressing both theoretical and practical aspects. Together, these studies develop a narrative that underscores the importance of interpretability in machine learning for enhancing the generalizability of models. Additionally, it focuses on the development of physics-aware machine learning models to achieve this objective. Each piece of research, distinct in its application, contributes to a unified theme of enhancing the interpretability, generalizability, and practical utility of machine learning in the realm of physics. This body of work advances our understanding of how machine learning can be effectively applied to physics problems and offers new perspectives and methodologies that can aid in many aspects of scientific exploration and discovery.
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