Data Driven Analysis and Modeling of Turbulent Flows

Karthik (Director Duraisamy

ISBN 10: 0323950434 / ISBN 13: 9780323950435

Verlag: Elsevier Science & Technology Jun 2025, 2025

Beschreibung

Neuware - Data-driven Analysis and Modeling of Turbulent Flows provides an integrated treatment of modern data-driven methods to describe, control, and predict turbulent flows through the lens of both physics and data science.The book is organized into three parts:¿ Exploration of techniques for discovering coherent structures within turbulent flows, introducing advanced decomposition methods¿ Methods for estimation and control using data assimilation and machine learning approaches¿ Finally, novel modeling techniques that combine physical insights with machine learningThis book is intended for students, researchers, and practitioners in fluid mechanics, though readers from related fields such as applied mathematics, computational science, and machine learning will find it also of interest.

Bestandsnummer des Verkäufers 9780323950435

Bibliografische Details

• Titel: Data Driven Analysis and Modeling of Turbulent Flows
• Verlag: Elsevier Science & Technology Jun 2025
• Erscheinungsjahr: 2025
• Sprache: Englisch
• ISBN-10: 0323950434
• ISBN-13: 9780323950435
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 228B x 155 H x 23 T Millimeter Breite: 228 mm, Höhe: 155 mm, Tiefe: 23 mm
• Gewicht: 660 Gramm

Über diesen Titel

Inhaltsangabe

Data-driven Analysis and Modeling of Turbulent Flows provides an integrated treatment of modern data-driven methods to describe, control, and predict turbulent flows through the lens of both physics and data science. The book is organized into three parts: • Exploration of techniques for discovering coherent structures within turbulent flows, introducing advanced decomposition methods • Methods for estimation and control using data assimilation and machine learning approaches • Finally, novel modeling techniques that combine physical insights with machine learning This book is intended for students, researchers, and practitioners in fluid mechanics, though readers from related fields such as applied mathematics, computational science, and machine learning will find it also of interest.

Über die Autorin bzw. den Autor

Karthik Duraisamy is a professor of Aerospace Engineering and the director of the Michigan Institute for Computational Discovery at the University of Michigan, Ann Arbor, USA. His research interests are in data-driven and reduced order modeling, statistical inference, numerical methods, and Generative AI with application to fluid flows. He is also the founder and Chief Scientist of the Silicon Valley startup Geminus.AI which is focused on physics informed AI for industrial decision-making.