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deep learning based forward modeling von wang yinpeng (41 Ergebnisse)
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Deep Learning-based Forward Modeling and Inversion Techniques for Computational Physics Problems
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Deep Learning-based Forward Modeling and Inversion Techniques for Computational Physics Problems
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Deep learning-based forward modeling and inversion techniques for computational physics problems
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Deep Learning-based Forward Modeling and Inversion Techniques for Computational Physics Problems
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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
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Deep Learning-based Forward Modeling and Inversion Techniques for Computational Physics Problems
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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
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Deep Learning-based Forward Modeling and Inversion Techniques for Computational Physics Problems
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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
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Deep Learning-based Forward Modeling and Inversion Techniques for Computational Physics Problems
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Deep Learning-based Forward Modeling and Inversion Techniques for Computational Physics Problems
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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems (Hardcover)
Sprache: Englisch
Verlag: Taylor & Francis Ltd, London, 2023
ISBN 10: 1032502983 ISBN 13: 9781032502984
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Hardcover. Zustand: 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
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Taschenbuch. Zustand: Neu. Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems | Yinpeng Wang (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2025 | CRC Press | EAN 9781032503035 | Verantwortliche Person für die EU: Taylor & Francis Verlag GmbH, Kaufingerstr. 24, 80331 München, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu.
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Deep Learning-based Forward Modeling and Inversion Techniques for Computational Physics Problems
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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
Sprache: Englisch
Verlag: Taylor and Francis Ltd, GB, 2023
ISBN 10: 1032502983 ISBN 13: 9781032502984
Hardback. Zustand: 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
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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
Sprache: Englisch
Verlag: Taylor and Francis Ltd, GB, 2023
ISBN 10: 1032502983 ISBN 13: 9781032502984
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In den WarenkorbHardback. Zustand: 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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Zustand: 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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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
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Zustand: New. 2023. 1st Edition. Hardcover. . . . . . Books ship from the US and Ireland.
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Deep Learning-Based Forward Modeling and Inversion Techniques for Computational Physics Problems
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In den WarenkorbHardcover. Zustand: 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
Sprache: Englisch
Verlag: Taylor and Francis Ltd, GB, 2023
ISBN 10: 1032502983 ISBN 13: 9781032502984
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Hardback. Zustand: 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
Sprache: Englisch
Verlag: Taylor and Francis Ltd, GB, 2023
ISBN 10: 1032502983 ISBN 13: 9781032502984
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In den WarenkorbHardback. Zustand: 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.
