Optimal Experimental Design For Parameter Identification And Model Selection

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Optimal Experimental Design For Parameter Identification And Model Selection

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Author : René Schenkendorf
language : en
Publisher:
Release Date : 2014

Optimal Experimental Design For Parameter Identification And Model Selection written by René Schenkendorf and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.

Model Based Parameter Estimation

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Author : Hans Georg Bock
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-02-26

Model Based Parameter Estimation written by Hans Georg Bock and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-02-26 with Mathematics categories.

This judicious selection of articles combines mathematical and numerical methods to apply parameter estimation and optimum experimental design in a range of contexts. These include fields as diverse as biology, medicine, chemistry, environmental physics, image processing and computer vision. The material chosen was presented at a multidisciplinary workshop on parameter estimation held in 2009 in Heidelberg. The contributions show how indispensable efficient methods of applied mathematics and computer-based modeling can be to enhancing the quality of interdisciplinary research. The use of scientific computing to model, simulate, and optimize complex processes has become a standard methodology in many scientific fields, as well as in industry. Demonstrating that the use of state-of-the-art optimization techniques in a number of research areas has much potential for improvement, this book provides advanced numerical methods and the very latest results for the applications under consideration.

Identification Of Parametric Models

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Author : Eric Walter
language : en
Publisher:
Release Date : 1997-01-14

Identification Of Parametric Models written by Eric Walter and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-01-14 with Computers categories.

The presentation of a coherent methodology for the estimation of the parameters of mathematical models from experimental data is examined in this volume. Many topics are covered including the choice of the structure of the mathematical model, the choice of a performance criterion to compare models, the optimization of this performance criterion, the evaluation of the uncertainty in the estimated parameters, the design of experiments so as to get the most relevant data and the critical analysis of results. There are also several features unique to the work such as an up-to-date presentation of the methodology for testing models for identifiability and distinguishability and a comprehensive treatment of parametric optimization which includes greater consider ation of numerical aspects and which examines recursive and non-recursive methods for linear and nonlinear models.

Robust Model Selection

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Author : Moritz Schulze
language : en
Publisher:
Release Date : 2020

Robust Model Selection written by Moritz Schulze and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.

Considering the competitive and strongly regulated pharmaceutical industry, mathematical modeling and process systems engineering might be useful tools for implementing quality by design (QbD) and quality by control (QbC) strategies for low-cost but high-quality drugs. However, a crucial task in modeling (bio)pharmaceutical manufacturing processes is the reliable identification of model candidates from a set of various model hypotheses. To identify the best experimental design suitable for a reliable model selection and system identification is challenging for nonlinear (bio)pharmaceutical process models in general. This paper is the first to exploit differential flatness for model selection problems under uncertainty, and thus translates the model selection problem to advanced concepts of systems theory and controllability aspects, respectively. Here, the optimal controls for improved model selection trajectories are expressed analytically with low computational costs. We further demonstrate the impact of parameter uncertainties on the differential flatness-based method and provide an effective robustification strategy with the point estimate method for uncertainty quantification. In a simulation study, we consider a biocatalytic reaction step simulating the carboligation of aldehydes, where we successfully derive optimal controls for improved model selection trajectories under uncertainty.

Optimal Design

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Author : S. Silvey
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09

Optimal Design written by S. Silvey and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Science categories.

Prior to the 1970's a substantial literature had accumulated on the theory of optimal design, particularly of optimal linear regression design. To a certain extent the study of the subject had been piecemeal, different criteria of optimality having been studied separately. Also to a certain extent the topic was regarded as being largely of theoretical interest and as having little value for the practising statistician. However during this decade two significant developments occurred. It was observed that the various different optimality criteria had several mathematical properties in common; and general algorithms for constructing optimal design measures were developed. From the first of these there emerged a general theory of remarkable simplicity and the second at least raised the possibility that the theory would have more practical value. With respect to the second point there does remain a limiting factor as far as designs that are optimal for parameter estimation are concerned, and this is that the theory assumes that the model be collected is known a priori. This of course underlying data to is seldom the case in practice and it often happens that designs which are optimal for parameter estimation allow no possibility of model validation. For this reason the theory of design for parameter estimation may well have to be combined with a theory of model validation before its practical potential is fully realized. Nevertheless discussion in this monograph is limited to the theory of design optimal for parameter estimation.

Network Bioscience 2nd Edition

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Author : Marco Pellegrini
language : en
Publisher: Frontiers Media SA
Release Date : 2020-03-27

Network Bioscience 2nd Edition written by Marco Pellegrini and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-27 with categories.

Network science has accelerated a deep and successful trend in research that influences a range of disciplines like mathematics, graph theory, physics, statistics, data science and computer science (just to name a few) and adapts the relevant techniques and insights to address relevant but disparate social, biological, technological questions. We are now in an era of 'big biological data' supported by cost-effective high-throughput genomic, transcriptomic, proteomic, metabolomic data collection techniques that allow one to take snapshots of the cells' molecular profiles in a systematic fashion. Moreover recently, also phenotypic data, data on diseases, symptoms, patients, etc. are being collected at nation-wide level thus giving us another source of highly related (causal) 'big data'. This wealth of data is usually modeled as networks (aka binary relations, graphs or webs) of interactions, (including protein-protein, metabolic, signaling and transcription-regulatory interactions). The network model is a key view point leading to the uncovering of mesoscale phenomena, thus providing an essential bridge between the observable phenotypes and 'omics' underlying mechanisms. Moreover, network analysis is a powerful 'hypothesis generation' tool guiding the scientific cycle of 'data gathering', 'data interpretation, 'hypothesis generation' and 'hypothesis testing'. A major challenge in contemporary research is the synthesis of deep insights coming from network science with the wealth of data (often noisy, contradictory, incomplete and difficult to replicate) so to answer meaningful biological questions, in a quantifiable way using static and dynamic properties of biological networks.

Asymptotic Expansions Of Integrals

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Author : Norman Bleistein
language : en
Publisher: Courier Corporation
Release Date : 1986-01-01

Asymptotic Expansions Of Integrals written by Norman Bleistein and has been published by Courier Corporation this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986-01-01 with Mathematics categories.

Excellent introductory text, written by two experts, presents a coherent and systematic view of principles and methods. Topics include integration by parts, Watson's lemma, LaPlace's method, stationary phase, and steepest descents. Additional subjects include the Mellin transform method and less elementary aspects of the method of steepest descents. 1975 edition.

Optimal Measurement Methods For Distributed Parameter System Identification

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Author : Dariusz Ucinski
language : en
Publisher: CRC Press
Release Date : 2004-08-27

Optimal Measurement Methods For Distributed Parameter System Identification written by Dariusz Ucinski and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-08-27 with Mathematics categories.

For dynamic distributed systems modeled by partial differential equations, existing methods of sensor location in parameter estimation experiments are either limited to one-dimensional spatial domains or require large investments in software systems. With the expense of scanning and moving sensors, optimal placement presents a critical problem.

Optimal Experimental Design With R

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Author : Dieter Rasch
language : en
Publisher: CRC Press
Release Date : 2011-05-18

Optimal Experimental Design With R written by Dieter Rasch and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-05-18 with Mathematics categories.

Experimental design is often overlooked in the literature of applied and mathematical statistics: statistics is taught and understood as merely a collection of methods for analyzing data. Consequently, experimenters seldom think about optimal design, including prerequisites such as the necessary sample size needed for a precise answer for an experi

Dynamic Model Development Methods Theory And Applications

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Author : S. Macchietto
language : en
Publisher: Elsevier
Release Date : 2003-08-04

Dynamic Model Development Methods Theory And Applications written by S. Macchietto and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-08-04 with Technology & Engineering categories.

Detailed mathematical models are increasingly being used by companies to gain competitive advantage through such applications as model-based process design, control and optimization. Thus, building various types of high quality models for processing systems has become a key activity in Process Engineering. This activity involves the use of several methods and techniques including model solution techniques, nonlinear systems identification, model verification and validation, and optimal design of experiments just to name a few. In turn, several issues and open-ended problems arise within these methods, including, for instance, use of higher-order information in establishing parameter estimates, establishing metrics for model credibility, and extending experiment design to the dynamic situation. The material covered in this book is aimed at allowing easier development and full use of detailed and high fidelity models. Potential applications of these techniques in all engineering disciplines are abundant, including applications in chemical kinetics and reaction mechanism elucidation, polymer reaction engineering, and physical properties estimation. On the academic side, the book will serve to generate research ideas. Contains wide coverage of statistical methods applied to process modelling Serves as a recent compilation of dynamic model building tools Presents several examples of applying advanced statistical and modelling methods to real process systems problems