Emulation Complex Fluid Flows by Wang Xingjian (16 results)

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New.

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Condition: New.

Condition: New.

Hardcover. Condition: Brand New. 145 pages. 9.44x6.69x9.61 inches. In Stock.

Condition: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

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Condition: New. Prof. Xingjian Wang received his Ph.D. from the Georgia Institute of Technology in 2016 and is currently associate professor in the Department of Energy and Power at Tsinghua University. He previously served as assistant profe.

Condition: New.

Condition: New.

Hardcover. Condition: new. Hardcover. While artificial intelligence has made significant strides in imaging and natural language processing, its utilization in engineering science remains relatively new. This book aims to introduce machine learning techniques to facilitate the emulation of complex fluid flows. The work focuses on projection-based reduced-order models (ROMs) that condense high-dimensional data into a low-dimensional subspace by leveraging principal components. Techniques like proper orthogonal decomposition (POD) and convolutional autoencoder (CAE) are utilized to configure this subspace, establishing a functional mapping between input parameters and solution fields. The applicability of POD-based ROMs for spatial and spatiotemporal problems are explored across various engineering scenarios, including flow past a cylinder, supercritical turbulent flows, and hydrogen-blended combustion. To capture intricate dynamics, common POD, kernel-smoothed POD, and common kernel-smoothed POD methods are developed in sequence. Additionally, the effectiveness of POD and CAE in capturing nonlinear features are compared. This book is designed to benefit graduate students and researchers interested in the intersection of data and engineering sciences. This series is essential to meet the growing demand for structured and accessible resources that guide the integration of machine learning (ML) into engineering and technology. The series will provide a comprehensive resource that bridges the gap between theory and practice, making it invaluable for students, researchers, and professionals looking to leverage ML in their work. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Condition: new. Hardcover. While artificial intelligence has made significant strides in imaging and natural language processing, its utilization in engineering science remains relatively new. This book aims to introduce machine learning techniques to facilitate the emulation of complex fluid flows. The work focuses on projection-based reduced-order models (ROMs) that condense high-dimensional data into a low-dimensional subspace by leveraging principal components. Techniques like proper orthogonal decomposition (POD) and convolutional autoencoder (CAE) are utilized to configure this subspace, establishing a functional mapping between input parameters and solution fields. The applicability of POD-based ROMs for spatial and spatiotemporal problems are explored across various engineering scenarios, including flow past a cylinder, supercritical turbulent flows, and hydrogen-blended combustion. To capture intricate dynamics, common POD, kernel-smoothed POD, and common kernel-smoothed POD methods are developed in sequence. Additionally, the effectiveness of POD and CAE in capturing nonlinear features are compared. This book is designed to benefit graduate students and researchers interested in the intersection of data and engineering sciences. This series is essential to meet the growing demand for structured and accessible resources that guide the integration of machine learning (ML) into engineering and technology. The series will provide a comprehensive resource that bridges the gap between theory and practice, making it invaluable for students, researchers, and professionals looking to leverage ML in their work. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condition: new. Hardcover. While artificial intelligence has made significant strides in imaging and natural language processing, its utilization in engineering science remains relatively new. This book aims to introduce machine learning techniques to facilitate the emulation of complex fluid flows. The work focuses on projection-based reduced-order models (ROMs) that condense high-dimensional data into a low-dimensional subspace by leveraging principal components. Techniques like proper orthogonal decomposition (POD) and convolutional autoencoder (CAE) are utilized to configure this subspace, establishing a functional mapping between input parameters and solution fields. The applicability of POD-based ROMs for spatial and spatiotemporal problems are explored across various engineering scenarios, including flow past a cylinder, supercritical turbulent flows, and hydrogen-blended combustion. To capture intricate dynamics, common POD, kernel-smoothed POD, and common kernel-smoothed POD methods are developed in sequence. Additionally, the effectiveness of POD and CAE in capturing nonlinear features are compared. This book is designed to benefit graduate students and researchers interested in the intersection of data and engineering sciences. This series is essential to meet the growing demand for structured and accessible resources that guide the integration of machine learning (ML) into engineering and technology. The series will provide a comprehensive resource that bridges the gap between theory and practice, making it invaluable for students, researchers, and professionals looking to leverage ML in their work. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.