Emulation of Complex Fluid Flows
De Gruyter (Verlag)
978-3-11-163135-6 (ISBN)
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 professor in the Department of Mechanical and Civil Engineering at the Florida Institute of Technology. His research focuses on the interdisciplinary study of engineering science and machine learning, particularly in developing reduced-order models and analyzing complex fluid flows and combustion under extreme conditions. Dr. Wang has received multiple awards, including the 2020 iLASS Asia Best Paper Award and the 2019 SPES Award from the American Statistical Society. His contributions to the field are well-recognized, with several articles featured as Editor's Picks and highlighted on the front cover of Physics of Fluids.
Prof. Vigor Yang is professor of aerospace engineering and a faculty member of the Machine Learning PhD Program at the Georgia Institute of Technology. He is also the founding director of Georgia Tech's James C. Wu Laboratory of Artificial Intelligence in Technology, Engineering, and Computing (ArTEC). Prof. Yang's research lies at the interface between engineering and data sciences, driving forward the integration of artificial intelligence and engineering disciplines for cutting-edge solutions. His extensive body of work includes advancements in thermal-fluid dynamics and propulsion, with a strong emphasis on leveraging machine learning to enhance these areas. He is a member of the U.S. National Academy of Engineering, an academician of the Academia Sinica, and a foreign member of the Chinese Academy of Engineering and the Indian National Academy of Engineering
| Erscheinungsdatum | 16.10.2025 |
|---|---|
| Reihe/Serie | Machine Learning in Science, Technology, Engineering and Mathematics ; 1 |
| Zusatzinfo | 4 b/w and 54 col. ill., 18 b/w and 18 col. tbl. |
| Verlagsort | Berlin/Boston |
| Sprache | englisch |
| Maße | 170 x 240 mm |
| Gewicht | 371 g |
| Themenwelt | Mathematik / Informatik ► Informatik |
| Naturwissenschaften ► Physik / Astronomie | |
| Schlagworte | Data-Driven Methods in Thermal-Fluid Engineering Sciences • Datengetriebene Methoden in den Thermalfluid-Ingenieurwissenschaften • Emulation of Spatio-Temporally Evolving Problems • Emulation von raum-zeitlich sich entwickelnden Problemen • Machine Learning of Thermal-Fluid Dynamics. • Maschinelles Lernen der Thermalfluiddynamik • Modellierung reduzierter Ordnung • Projection-Based Machine Learning • Projektionsbasiertes maschinelles Lernen • Reduced-Order Modeling |
| ISBN-10 | 3-11-163135-4 / 3111631354 |
| ISBN-13 | 978-3-11-163135-6 / 9783111631356 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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