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Deep Learning Based Forward Modeling by Wang Yinpeng (40 results)
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Condition: As New. Unread book in perfect condition.
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Condition: New. pages cm.
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Condition: New. pages cm First edition Includes bibliographical references and index.
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Deep Learning-based Forward Modeling and Inversion Techniques for Computational Physics Problems
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Condition: New. pages cm.
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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
Language: English
Published by Taylor & Francis Ltd, 2025
ISBN 10: 1032503033 ISBN 13: 9781032503035
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
Paperback / softback. Condition: New. New copy - Usually dispatched within 4 working days.
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Deep Learning-based Forward Modeling and Inversion Techniques for Computational Physics Problems
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Paperback. Condition: Brand New. 194 pages. 9.18x6.12x9.21 inches. In Stock.
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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Deep Learning-based Forward Modeling and Inversion Techniques for Computational Physics Problems
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Condition: As New. Unread book in perfect condition.
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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
Language: English
Published by CRC Press 2023-07-06, 2023
ISBN 10: 1032502983 ISBN 13: 9781032502984
Hardcover. Condition: New.
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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
Language: English
Published by Taylor & Francis Ltd, 2023
ISBN 10: 1032502983 ISBN 13: 9781032502984
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New. New copy - Usually dispatched within 4 working days. 185.
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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems (Hardcover)
Language: English
Published by Taylor & Francis Ltd, London, 2023
ISBN 10: 1032502983 ISBN 13: 9781032502984
Hardcover. Condition: new. Hardcover. This book investigates in detail the emerging deep learning (DL) technique in computational physics, assessing its promising potential to substitute conventional numerical solvers for calculating the fields in real-time. After good training, the proposed architecture can resolve both the forward computing and the inverse retrieve problems.Pursuing a holistic perspective, the book includes the following areas. The first chapter discusses the basic DL frameworks. Then, the steady heat conduction problem is solved by the classical U-net in Chapter 2, involving both the passive and active cases. Afterwards, the sophisticated heat flux on a curved surface is reconstructed by the presented Conv-LSTM, exhibiting high accuracy and efficiency. Additionally, a physics-informed DL structure along with a nonlinear mapping module are employed to obtain the space/temperature/time-related thermal conductivity via the transient temperature in Chapter 4. Finally, in Chapter 5, a series of the latest advanced frameworks and the corresponding physics applications are introduced.As deep learning techniques are experiencing vigorous development in computational physics, more people desire related reading materials. This book is intended for graduate students, professional practitioners, and researchers who are interested in DL for computational physics. This book investigates in detail the emerging deep learning (DL) technique in computational physics, assessing its promising potential to substitute conventional numerical solvers for calculating the fields in real-time. After good training, the proposed architecture can resolve both the forward computing and the inverse retrieve problems. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Deep Learning-based Forward Modeling and Inversion Techniques for Computational Physics Problems
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
Language: English
Published by Taylor & Francis Ltd, 2023
ISBN 10: 1032502983 ISBN 13: 9781032502984
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Seller rating 5 out of 5 stars
First Edition
Condition: New. 2023. 1st Edition. Hardcover. . . . . .
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Hardcover. Condition: Brand New. 232 pages. 9.19x6.13x0.32 inches. In Stock.
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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
Language: English
Published by Taylor and Francis Ltd, GB, 2023
ISBN 10: 1032502983 ISBN 13: 9781032502984
Hardback. Condition: New. This book investigates in detail the emerging deep learning (DL) technique in computational physics, assessing its promising potential to substitute conventional numerical solvers for calculating the fields in real-time. After good training, the proposed architecture can resolve both the forward computing and the inverse retrieve problems.Pursuing a holistic perspective, the book includes the following areas. The first chapter discusses the basic DL frameworks. Then, the steady heat conduction problem is solved by the classical U-net in Chapter 2, involving both the passive and active cases. Afterwards, the sophisticated heat flux on a curved surface is reconstructed by the presented Conv-LSTM, exhibiting high accuracy and efficiency. Additionally, a physics-informed DL structure along with a nonlinear mapping module are employed to obtain the space/temperature/time-related thermal conductivity via the transient temperature in Chapter 4. Finally, in Chapter 5, a series of the latest advanced frameworks and the corresponding physics applications are introduced.As deep learning techniques are experiencing vigorous development in computational physics, more people desire related reading materials. This book is intended for graduate students, professional practitioners, and researchers who are interested in DL for computational physics.
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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
Language: English
Published by Taylor and Francis Ltd, GB, 2023
ISBN 10: 1032502983 ISBN 13: 9781032502984
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New. This book investigates in detail the emerging deep learning (DL) technique in computational physics, assessing its promising potential to substitute conventional numerical solvers for calculating the fields in real-time. After good training, the proposed architecture can resolve both the forward computing and the inverse retrieve problems.Pursuing a holistic perspective, the book includes the following areas. The first chapter discusses the basic DL frameworks. Then, the steady heat conduction problem is solved by the classical U-net in Chapter 2, involving both the passive and active cases. Afterwards, the sophisticated heat flux on a curved surface is reconstructed by the presented Conv-LSTM, exhibiting high accuracy and efficiency. Additionally, a physics-informed DL structure along with a nonlinear mapping module are employed to obtain the space/temperature/time-related thermal conductivity via the transient temperature in Chapter 4. Finally, in Chapter 5, a series of the latest advanced frameworks and the corresponding physics applications are introduced.As deep learning techniques are experiencing vigorous development in computational physics, more people desire related reading materials. This book is intended for graduate students, professional practitioners, and researchers who are interested in DL for computational physics.
-
Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
Language: English
Published by Taylor & Francis Ltd, 2023
ISBN 10: 1032502983 ISBN 13: 9781032502984
Condition: New. 2023. 1st Edition. Hardcover. . . . . . Books ship from the US and Ireland.
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Condition: New. Yinpeng Wang received the B.S. degree in Electronic and Information Engineering from Beihang University, Beijing, China in 2020, where he is currently pursuing his M.S. degree in Electronic Science and Technology. Mr. Wang focuses on the.
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Hardcover. Condition: Brand New. 232 pages. 9.19x6.13x0.32 inches. In Stock.
-
Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
Language: English
Published by Taylor and Francis Ltd, GB, 2023
ISBN 10: 1032502983 ISBN 13: 9781032502984
Hardback. Condition: New. This book investigates in detail the emerging deep learning (DL) technique in computational physics, assessing its promising potential to substitute conventional numerical solvers for calculating the fields in real-time. After good training, the proposed architecture can resolve both the forward computing and the inverse retrieve problems.Pursuing a holistic perspective, the book includes the following areas. The first chapter discusses the basic DL frameworks. Then, the steady heat conduction problem is solved by the classical U-net in Chapter 2, involving both the passive and active cases. Afterwards, the sophisticated heat flux on a curved surface is reconstructed by the presented Conv-LSTM, exhibiting high accuracy and efficiency. Additionally, a physics-informed DL structure along with a nonlinear mapping module are employed to obtain the space/temperature/time-related thermal conductivity via the transient temperature in Chapter 4. Finally, in Chapter 5, a series of the latest advanced frameworks and the corresponding physics applications are introduced.As deep learning techniques are experiencing vigorous development in computational physics, more people desire related reading materials. This book is intended for graduate students, professional practitioners, and researchers who are interested in DL for computational physics.
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Buch. Condition: Neu. Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems | Yinpeng Wang (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2023 | CRC Press | EAN 9781032502984 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
