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Choi, Tony Doungho; Eslinger, Owen; Kelley, C. T.; David, J. W.; Etheridge, Mark

doi:10.1023/a:1010071821464

Cited by 46 publications

(9 citation statements)

References 25 publications

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“…II.4;115]. This type of hidden constraint has been observed for a parameter estimation problem described in [37,50,51]. For some values of the parameters, the ODE considered is stiff and its numerical solution using an explicit method fails, [50,83].…”

Section: Hidden Constraintsmentioning

confidence: 97%

“…The term hidden constraint often refers to the case that the computer code used to evaluate the objective function fails to return a value, e.g. [36,37,55,56,77;82,Ch. 4].…”

Section: Hidden Constraintsmentioning

confidence: 99%

“…Derivative-free optimization has been applied for several problems that involve systems of ODEs, [37,50,51,57]. However, the fisheries literature includes few examples of the application of derivative-free optimization methods.…”

Section: Related Topicsmentioning

confidence: 99%

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A parametrized stock-recruitment relationship derived from a slow-fast population dynamic model

Schaarschmidt

Steihaug

Subbey

2018

Mathematics and Computers in Simulation

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AcknowledgementsMy deepest and sincere thank you goes to Trond Steihaug, for his support and guidance throughout my research work. I appreciate that you were never tired of giving me advice for any problem, often in the form of some metaphor.I would like to thank Sam Subbey for his support and for many fruitful discussions. The atmosphere at the Institute of Informatics was delightful, and I would like to thank the administration for letting me stay at my working place until submission. Many of my colleagues have made the last years a great experience. To mention but a few, Joanna Bauer, Arne Klein and Marika Ivanova, I enjoyed your company.Thank you to my friends for listening to me talking about my research and for making sure that I did not forget life outside of academia. Thank you for proofreading parts of my work.Sindre Saether has been very supportive. Danke, dass du mir den Rücken frei gehalten hast. Ohne dich wäre der Kühlschrank leer und die Kleidung in der Wäsche geblieben.To my family: Takk til Saether-familien for at jeg får lov til å vaere en del av familien. Marianne, danke dafür, dass du immer mein Bestes willst. Manche Leute haben den Grundstein für etwas gelegt, das damals noch mehr oder weniger unabsehbar in der Zukunft lag. Mein Dank gebührt auch dir, Hannelore, denn du warst wichtig für mich. Den wichtigsten Stein aber hast du gelegt, Bodo. Ich hätte gerne mit dir über mein Thema geredet und ich wünschte, du wüsstest, wie dankbar ich dir bin.ii Acknowledgements AbstractThe parent-progeny (adult fish-juvenile) relationship is central to understanding the dynamics of fish populations. Management and harvest decisions are based on the assumption of a stock-recruitment function that relates the number of adults to their progeny. Multi-stage population dynamic models provide a modelling framework for understanding this relationship, since they describe the dynamics of fish in several life stages (such as adults, eggs, larvae, and juveniles). Biological processes at various life stages usually evolve at distinct time scales.This thesis contains three papers, which address challenges in modelling and parameter estimation for multiple time-scale dynamics of stage structured populations. A major question is, whether a multi-stage population dynamic model supports the assumption of a stock-recruitment function.In the first paper, we address the parent-progeny relationship admitted by slow-fast systems of differential equations that model the dynamics of a fish population with two stages. We introduce a slow-fast population dynamic model which replicates several well-known stock-recruitment functions.Traditionally, the dynamics of fish populations are described by difference equations. In the second paper, discrete time models for several life stages are formulated. We demonstrate that a multi-stage model may not admit a stock-recruitment function. Sufficient conditions for the validity of two hypotheses about the existence and structure of a parent-progeny function are established.Parameters in populat...

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Section: Hidden Constraintsmentioning

confidence: 97%

“…The term hidden constraint often refers to the case that the computer code used to evaluate the objective function fails to return a value, e.g. [36,37,55,56,77;82,Ch. 4].…”

Section: Hidden Constraintsmentioning

confidence: 99%

See 1 more Smart Citation

A parametrized stock-recruitment relationship derived from a slow-fast population dynamic model

Schaarschmidt

Steihaug

Subbey

2018

Mathematics and Computers in Simulation

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“…[43][44][45][46][47][48][49] The algorithm optimizes functions using a decreasing sequence of finite difference steps to approximate the gradient. We have applied this method in the analysis of MEIS and CAICISS experiments through combination with ion scattering simulation codes.…”

Section: Structural Optimization Using Iffcomentioning

confidence: 99%

Structural analysis of the Cu(100)-p(2×2)-Sn surface using low and medium energy ion scattering spectroscopies

et al. 2011

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The atomic structure of the Cu(100)-p(2 × 2)-Sn surface has been studied using medium energy ion scattering (MEIS), co-axial impact collision ion scattering spectroscopy (CAICISS), low energy electron diffraction (LEED), and Auger electron spectroscopy (AES). The complex p(2 × 2)-based LEED pattern with antiphase domains was observed at an estimated Sn coverage of 0.21 ML, confirmed with AES. The existence of subsurface Sn atoms was immediately ruled out by the lack of blocking dips in the MEIS Sn scattered yield, whereas the initial analysis of the CAICISS data ruled out the three overlayer models, allowing quantitative fitting to focus on the Cu-Sn substitutional surface alloy model. The fitted model showed that the surface relaxed outward following Sn deposition and also demonstrated an out-of-plane shift of Sn atoms in the outermost layer.

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“…Through computer modeling, experimental validation, and robust optimal design strategies David [4] showed that it is possible to develop optimal design to produce optimal valve train systems. Choi [5] was interested in the elaboration of camshaft lobes profiles using implicit filtering algorithm helping parameter identification and optimization in automotive valve train design. Cardona [6] presented a methodology to design cams for motor engine valve trains using a constrained optimization algorithm in order to maximize the time integral of the valve area opened to gas flow.…”

Section: Introductionmentioning

confidence: 99%

Design Optimization of Cam & Follower Mechanism of an Internal Combustion Engine for Improving the Engine Efficiency

Mali¹,

Maskar²,

Gawande³

et al. 2012

MME

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The current cam and follower mechanism in four stroke internal combustion engine employs a flat follower. In this work an attempt is made to change the flat face of follower to a curved face follower, so that the required point contact can be achieved. As line contact between existing cam and follower mechanism results in high frictional losses which results in low mechanical efficiency. It is observed that the frequency of vibration in the existing and modified cam and follower mechanism remains almost same. The finite element approach is used to perform the analysis.

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Untitled

Cited by 46 publications

References 25 publications

A parametrized stock-recruitment relationship derived from a slow-fast population dynamic model

A parametrized stock-recruitment relationship derived from a slow-fast population dynamic model

Structural analysis of the Cu(100)-p(2×2)-Sn surface using low and medium energy ion scattering spectroscopies

Design Optimization of Cam & Follower Mechanism of an Internal Combustion Engine for Improving the Engine Efficiency

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Untitled

Cited by 46 publications

References 25 publications

A parametrized stock-recruitment relationship derived from a slow-fast population dynamic model

A parametrized stock-recruitment relationship derived from a slow-fast population dynamic model

Structural analysis of the Cu(100)-p(2×2)-Sn surface using low and medium energy ion scattering spectroscopies

Design Optimization of Cam &amp; Follower Mechanism of an Internal Combustion Engine for Improving the Engine Efficiency

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Design Optimization of Cam & Follower Mechanism of an Internal Combustion Engine for Improving the Engine Efficiency