% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@PHDTHESIS{Elsheikh:229154,
author = {Elsheikh, Atiyah Mohamed Gamal},
othercontributors = {Naumann, Uwe},
title = {{M}odelica based computational tools for sensitivity
analysis via automatic differentiation},
address = {Aachen},
publisher = {Publikationsserver der RWTH Aachen University},
reportid = {RWTH-CONV-144127},
pages = {XX, 164 S. : Ill., graph. Darst.},
year = {2012},
note = {Prüfungsjahr: 2012. - Publikationsjahr: 2014; Aachen,
Techn. Hochsch., Diss., 2012},
abstract = {This work is mainly concerned with sensitivity analysis of
DAE-based models described by the modern object-oriented
modeling language Modelica. In this context, an automatic
differentiation tool named as ADModelica is presented. It
fully employs Modelica-based compiler techniques forming a
new automatic differentiation approach for non-causal
equation-based languages. Already existing open-source
compiler tools are utilized for reducing implementation
efforts. A generated output model efficiently represents a
sensitivity equation system by which parameter sensitivities
can be simulated using any existing Modelica simulation
environment. The resulting tool has been successfully
applied on high-level Modelica models in the field of
Systems Biology. In benchmark examples, the performance of
the generated models are better than applying common finite
difference methods in terms of accuracy and runtime
performance. Moreover, the representation of these models
permits the exploitation of structural characteristics of
sensitivity equation systems for significantly improved
runtime performance on supercomputer clusters.Using
ADModelica, several sensitivity analysis application studies
of computationally, algorithmically and technically
challenging nature have been performed towards the
realization of stable efficient parameter estimation process
of large and badly-scaled dynamical models. These studies
cover among others: • The examination of several global
multistart optimization methods w.r.t. results quality and
implementation efforts, in particular the design of new
derivative-based hybrid heuristic strategies• The
determination of confidence regions of model parameters via
identifiability analysis techniques based on linearized
statistics and Monte Carlo bootstrap methods.Within this
work furtherModelica-based both domain-dependent and
domain-independent computational tools have been implemented
such as:• A compact Modelica library for simplified
kinetics for modeling complex reaction systems through which
model families can be easily specified• A tool for
visualizing scaled parameter sensitivities within a
supervised master thesis • A Modelica-based editor for
modeling biochemical reaction networks within a
collaborative work with collegesFinally, this thesis also
covers theoretical studies concerning the differential and
the structural index of a DAE system and the corresponding
sensitivity equation system with an interesting
mathematically proven conclusion about their relationship.},
keywords = {Automatische Differentiation (SWD) / Sensitivitätsanalyse
(SWD) / Systembiologie (SWD) / Modelica (SWD)},
cin = {120000 / 123010},
ddc = {004},
cid = {$I:(DE-82)120000_20140620$ / $I:(DE-82)123010_20140620$},
typ = {PUB:(DE-HGF)11},
urn = {urn:nbn:de:hbz:82-opus-47677},
url = {https://publications.rwth-aachen.de/record/229154},
}
guest ::