Handbook of Dynamic Data Driven Applications Systems Volume 2
This Second Volume in the series Handbook of Dynamic Data Driven Applications Systems (DDDAS) expands the scope of the methods and the application areas presented in the first Volume and aims to provide additional and extended content of the increasing set of science and engineering advances for new...
| Other Authors: | , , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing
2023, 2023
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| Edition: | 1st ed. 2023 |
| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
| Summary: | This Second Volume in the series Handbook of Dynamic Data Driven Applications Systems (DDDAS) expands the scope of the methods and the application areas presented in the first Volume and aims to provide additional and extended content of the increasing set of science and engineering advances for new capabilities enabled through DDDAS. The methods and examples of breakthroughs presented in the book series capture the DDDAS paradigm and its scientific and technological impact and benefits. The DDDAS paradigm and the ensuing DDDAS-based frameworks for systems’ analysis and design have been shown to engender new and advanced capabilities for understanding, analysis, and management of engineered, natural, and societal systems (“applications systems”), and for the commensurate wide set of scientific and engineering fields and applications, as well as foundational areas. The DDDAS book series aims to be a reference source ofmany of the important research and development efforts conducted under the rubric of DDDAS through the examples and case studies presented, either within their own field or other fields of study. DDDAS has proven to be a transformative technology as data has become the third part of the triad, with theory and computation as the other two parts. Initially DDDAS was part of control theory but as data have become ubiquitous there has been a paradigm shift, initiated by DDDAS, from simulation to effectively using data for prediction. John Cherniavsky, retired, as Division Director, NSF - US National Science Foundation The DDDAS paradigm integrates and enhances the deepest, most well-grounded foundations and tools - both from expert based methods and from learning-based methods, building well beyond more popular and limited forms of AI. This book provides a unique breadth and depth of scope across many science and technology fields, showing how DDDAS is an overarching concept that connects and unifies the wide diversity across these fields. Paul Werbos, retired, as Program Director, NSF - US National Science Foundation |
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| Physical Description: | X, 956 p. 316 illus., 281 illus. in color online resource |
| ISBN: | 9783031279867 |