000			03557nam a2200421Ia 4500
000			06998nam a22004575i 4500
001			978-3-031-27986-7
003			DE-He213
005			20240319121031.0
007			cr nn 008mamaa
008			230914s2023 sz | s |||| 0|eng d
020			_a9783031279867 _9978-3-031-27986-7
082			_a3.3
245			_aHandbook of Dynamic Data Driven Applications Systems _cedited by Frederica Darema, Erik P. Blasch, Sai Ravela, Alex J. Aved. _h[electronic resource] :
250			_a1st ed. 2023.
260			_aCham _bSpringer International Publishing _c2023
300			_aX, 956 p. 316 illus., 281 illus. in color. _bonline resource.
520			_aThis 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 of many 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 .
650			_aApplied Dynamical Systems. _935953
650			_aBig data. _935954
650			_aBig Data. _935955
650			_aComputer Modelling. _935956
650			_aComputer simulation. _935957
650			_aComputers, Special purpose. _935958
650			_aData Analysis and Big Data. _935959
650			_aDynamical systems. _935960
650			_aDynamical Systems. _935961
650			_aDynamics. _935962
650			_aNonlinear theories. _935963
650			_aQuantitative research. _935964
650			_aSpecial Purpose and Application-Based Systems. _935965
700			_aAved, Alex J. _935966
700			_aBlasch, Erik P. _935967
700			_aDarema, Frederica. _935968
700			_aRavela, Sai. _935969
856			_uhttps://doi.org/10.1007/978-3-031-27986-7
942			_cEBK _2ddc
999			_c15600 _d15600