Advances in High-Order Predictive Modeling: Methodologies and Illustrative Problems (Advances in Applied Mathematics)

Cacuci, Dan Gabriel

ISBN 10: 1032740566 / ISBN 13: 9781032740560

Verlag: Chapman and Hall/CRC, 2024

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Bibliografische Details

• Titel: Advances in High-Order Predictive Modeling: Methodologies and Illustrative Problems (Advances in Applied Mathematics)
• Verlag: Chapman and Hall/CRC
• Erscheinungsjahr: 2024
• Sprache: Englisch
• ISBN-10: 1032740566
• ISBN-13: 9781032740560
• Einband: Hardcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

Continuing the author's previous work on modeling, this book presents the most recent advances in high-order predictive modeling.

Über die Autorin bzw. den Autor

Dan Gabriel Cacuci is a Distinguished Professor Emeritus in the Department of Mechanical Engineering at the University of South Carolina and the Karlsruhe Institute of Technology, Germany. He received his PhD in applied physics, mechanical and nuclear engineering from Columbia University. He is also the recipient of many awards including four honorary doctorates, the Ernest Orlando Lawrence Memorial award from the U.S. Deptartment of Energy and the Arthur Holly Compton, Eugene P. Wigner and the Glenn Seaborg Awards from the American Nuclear Society. He was named an “Inaugural Highly Ranked Scholar” by Scholar GPS, being ranked #2 in the world in the field of Uncertainty Analysis, #5 in the world in the field of Sensitivity Analysis, and ranked in the top 0.05% of all scholars worldwide.

This is Dr. Cacuci’s fifth book for CRC Press. The others include, The Second-Order Adjoint Sensitivity Analysis Methodology (2018); Computational Methods for Data Evaluation and Assimilation with Ionel Michael Navon and Mihaela Ionescu-Bujor (2013); Sensitivity and Uncertainty Analysis, Volume I Applications to Large-Scale Systems (2003) and Volume II (2005) also with Mihaela Ionescu-Bujor and Michael Navon.

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