Emulation of Complex Fluid Flows

Name: Emulation of Complex Fluid Flows
Brand: De Gruyter
SKU: 9783111631356
Price: 799.27 PLN
Availability: InStock

Projection-Based Reduced-Order Modeling and Machine Learning

Autor: Xingjian Wang, Vigor Yang

Język:

Oprawa: Twarda

Wydawca: De Gruyter

Dostępność: Dostępna u dostawcy

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799.27 zł

While artificial intelligence has made significant strides in imaging and natural language processin...

Informacje o książce

Autor

Xingjian Wang, Vigor Yang

Język

Angielski

Oprawa

Książka - Twarda

Data wydania

2025

strony

135

EAN

9783111631356

Enbook ID

49153686

Wydawca

De Gruyter

Waga

403

Wymiary

170 x 240

Pełny opis

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.

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Emulation of Complex Fluid Flows

Projection-Based Reduced-Order Modeling and Machine Learning

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