Deep Learning and Computational Physics [electronic resource] / by Deep Ray, Orazio Pinti, Assad A. Oberai.

Por: Ray, Deep [author.]Colaborador(es): Pinti, Orazio [author.] | Oberai, Assad A [author.] | SpringerLink (Online service)Tipo de material: TextoTextoEditor: Cham : Springer Nature Switzerland : Imprint: Springer, 2024Edición: 1st ed. 2024Descripción: XVI, 152 p. 49 illus., 42 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783031593451Tema(s): Engineering -- Data processing | Machine learning | Big data | Computational intelligence | Engineering mathematics | Mathematical physics | Data Engineering | Machine Learning | Big Data | Computational Intelligence | Mathematical and Computational Engineering Applications | Theoretical, Mathematical and Computational PhysicsFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 620.00285 Clasificación LoC:TA345-345.5Recursos en línea: Libro electrónicoTexto
Contenidos:
Introduction -- Introduction to deep neural networks -- Residual neural networks -- Convolutional Neural Networks -- Solving PDEs with Neural Networks -- Operator Networks -- Generative Deep Learning.
En: Springer Nature eBookResumen: The main objective of this book is to introduce a student who is familiar with elementary math concepts to select topics in deep learning. It exploits strong connections between deep learning algorithms and the techniques of computational physics to achieve two important goals. First, it uses concepts from computational physics to develop an understanding of deep learning algorithms. Second, it describes several novel deep learning algorithms for solving challenging problems in computational physics, thereby offering someone who is interested in modeling physical phenomena with a complementary set of tools. It is intended for senior undergraduate and graduate students in science and engineering programs. It is used as a textbook for a course (or a course sequence) for senior-level undergraduate or graduate-level students. .
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Introduction -- Introduction to deep neural networks -- Residual neural networks -- Convolutional Neural Networks -- Solving PDEs with Neural Networks -- Operator Networks -- Generative Deep Learning.

The main objective of this book is to introduce a student who is familiar with elementary math concepts to select topics in deep learning. It exploits strong connections between deep learning algorithms and the techniques of computational physics to achieve two important goals. First, it uses concepts from computational physics to develop an understanding of deep learning algorithms. Second, it describes several novel deep learning algorithms for solving challenging problems in computational physics, thereby offering someone who is interested in modeling physical phenomena with a complementary set of tools. It is intended for senior undergraduate and graduate students in science and engineering programs. It is used as a textbook for a course (or a course sequence) for senior-level undergraduate or graduate-level students. .

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