000 03035nam a22005055i 4500
001 978-1-4614-7218-6
003 DE-He213
005 20140220082828.0
007 cr nn 008mamaa
008 130611s2013 xxu| s |||| 0|eng d
020 _a9781461472186
_9978-1-4614-7218-6
024 7 _a10.1007/978-1-4614-7218-6
_2doi
050 4 _aQC174.7-175.36
072 7 _aPHS
_2bicssc
072 7 _aPHDT
_2bicssc
072 7 _aSCI055000
_2bisacsh
082 0 4 _a621
_223
100 1 _aAbarbanel, Henry.
_eauthor.
245 1 0 _aPredicting the Future
_h[electronic resource] :
_bCompleting Models of Observed Complex Systems /
_cby Henry Abarbanel.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2013.
300 _aXVI, 238 p. 97 illus., 91 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUnderstanding Complex Systems,
_x1860-0832
505 0 _aPreface -- 1 An Overview; The Challenge of Complex Systems -- 2 Examples as a Guide to the Issues -- 3 General Formulation of Statistical Data Assimilation -- 4 Evaluating the Path Integral -- 5 Twin Experiments -- 6 Analysis of Experimental Data.
520 _aPredicting the Future: Completing Models of Observed Complex Systems provides a general framework for the discussion of model building and validation across a broad spectrum of disciplines. This is accomplished through the development of an exact path integral for use in transferring information from observations to a model of the observed system. Through many illustrative examples drawn from models in neuroscience, fluid dynamics, geosciences, and nonlinear electrical circuits, the concepts are exemplified in detail. Practical numerical methods for approximate evaluations of the path integral are explored, and their use in designing experiments and determining a model's consistency with observations is investigated. Using highly instructive examples, the problems of data assimilation and the means to treat them are clearly illustrated. This book will be useful for students and practitioners of physics, neuroscience, regulatory networks, meteorology and climate science, network dynamics, fluid dynamics, and other systematic investigations of complex systems.
650 0 _aPhysics.
650 0 _aNeurosciences.
650 0 _aComputer simulation.
650 1 4 _aPhysics.
650 2 4 _aStatistical Physics, Dynamical Systems and Complexity.
650 2 4 _aComplex Systems.
650 2 4 _aNumerical and Computational Physics.
650 2 4 _aSimulation and Modeling.
650 2 4 _aNeurosciences.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461472179
830 0 _aUnderstanding Complex Systems,
_x1860-0832
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-7218-6
912 _aZDB-2-PHA
999 _c95872
_d95872