New methods for estimation, modeling and validation of dynamical systems using automatic differentiation

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dc.contributor.advisor Junkins , John L . en_US
dc.contributor.committeeMember Palazzolo , Alan B . en_US
dc.creator Griffith , Daniel Todd en_US 2005 -02 -17T21 :00 :34Z 2014 -02 -19T18 :36 :04Z 2005 -02 -17T21 :00 :34Z 2014 -02 -19T18 :36 :04Z 2004 -12 en_US 2005 -02 -17T21 :00 :34Z
dc.identifier.uri http : / /hdl .handle .net /1969 .1 /1408
dc.description.abstract The main objective of this work is to demonstrate some new computational methods for estimation , optimization and modeling of dynamical systems that use automatic differentiation . Particular focus will be upon dynamical systems arising in Aerospace Engineering . Automatic differentiation is a recursive computational algorithm , which enables computation of analytically rigorous partial derivatives of any user -specified function . All associated computations occur , in the background without user intervention , as the name implies . The computational methods of this dissertation are enabled by a new automatic differentiation tool , OCEA (Object oriented Coordinate Embedding Method ) . OCEA has been recently developed and makes possible efficient computation and evaluation of partial derivatives with minimal user coding . The key results in this dissertation details the use of OCEA through a number of computational studies in estimation and dynamical modeling . Several prototype problems are studied in order to evaluate judicious ways to use OCEA . Additionally , new solution methods are introduced in order to ascertain the extended capability of this new computational tool . Computational tradeoffs are studied in detail by looking at a number of different applications in the areas of estimation , dynamical system modeling , and validation of solution accuracy for complex dynamical systems . The results of these computational studies provide new insights and indicate the future potential of OCEA in its further development . en_US
dc.format.extent 11948932 bytes
dc.format.medium electronic en_US
dc.format.mimetype application /pdf
dc.language.iso en _US en_US
dc.publisher Texas A &M University en_US
dc.subject automatic differentiation en_US
dc.title New methods for estimation , modeling and validation of dynamical systems using automatic differentiation en_US
dc.type Book en
dc.type.genre Electronic Dissertation en_US
dc.type.material text en_US
dc.format.digitalOrigin born digital en_US


New methods for estimation, modeling and validation of dynamical systems using automatic differentiation. Available electronically from http : / /hdl .handle .net /1969 .1 /1408 .

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