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31
Computer Experiments
, 1996
"... Introduction Deterministic computer simulations of physical phenomena are becoming widely used in science and engineering. Computers are used to describe the flow of air over an airplane wing, combustion of gasses in a flame, behavior of a metal structure under stress, safety of a nuclear reactor, a ..."
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Cited by 84 (5 self)
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Introduction Deterministic computer simulations of physical phenomena are becoming widely used in science and engineering. Computers are used to describe the flow of air over an airplane wing, combustion of gasses in a flame, behavior of a metal structure under stress, safety of a nuclear reactor, and so on. Some of the most widely used computer models, and the ones that lead us to work in this area, arise in the design of the semiconductors used in the computers themselves. A process simulator starts with a data structure representing an unprocessed piece of silicon and simulates the steps such as oxidation, etching and ion injection that produce a semiconductor device such as a transistor. A device simulator takes a description of such a device and simulates the flow of current through it under varying conditions to determine properties of the device such as its switching speed and the critical voltage at which it switches. A circuit simulator takes a list of devices and the
Modeling and simulation: tools for metabolic engineering
 Journal of Biotechnology
, 2002
"... Abstract: Mathematical modeling is one of the key methodologies of metabolic engineering. Based on a given metabolic model different computational tools for the simulation, data evaluation, systems analysis, prediction, design and optimization of metabolic systems have been developed. The currently ..."
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Cited by 20 (2 self)
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Abstract: Mathematical modeling is one of the key methodologies of metabolic engineering. Based on a given metabolic model different computational tools for the simulation, data evaluation, systems analysis, prediction, design and optimization of metabolic systems have been developed. The currently used metabolic modeling approaches can be subdivided into structural models, stoichiometric models, carbon flux models, stationary and nonstationary mechanistic models and models with gene regulation. However, the power of a model strongly depends on its basic modeling assumptions, the simplifications made and the data sources used. Model validation turns out to be particularly difficult for metabolic systems. The different modeling approaches are critically reviewed with respect to their potential and benefits for the metabolic engineering cycle. Several tools are discussed that have emerged from the different modeling approaches including structural pathway synthesis, stoichiometric pathway analysis, metabolic flux analysis, metabolic control analysis, optimization of regulatory architectures and the evaluation of rapid sampling experiments.
Simulation methods for optimal experimental design in systems biology
 Simulation
, 2003
"... To obtain a systemslevel understanding of a biological system, the authors conducted quantitative dynamic experiments from which the system structure and the parameters have to be deduced. Since biological systems have to cope with different environmental conditions, certain properties are often ro ..."
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Cited by 11 (1 self)
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To obtain a systemslevel understanding of a biological system, the authors conducted quantitative dynamic experiments from which the system structure and the parameters have to be deduced. Since biological systems have to cope with different environmental conditions, certain properties are often robust with respect to variations in some of the parameters. Hence, it is important to use optimal experimental design considerations in advance of the experiments to improve the information content of the measurements. Using the MAP–Kinase pathway as an example, the authors present a simulation study investigating the application of different optimality criteria. It is demonstrated that experimental design significantly improves the parameter estimation accuracy and also reveals difficulties in parameter estimation due to robustness.
Conjecturing Hidden Entities by Means of Simplicity and Conservation Laws: Machine Discovery in Chemistry
"... We show that combinatorial search, constrained by experimental evidence, domain knowledge, and simplicity, is sufficient to discover credible explanatory hypotheses in a scientific task of current importance. ..."
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Cited by 7 (0 self)
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We show that combinatorial search, constrained by experimental evidence, domain knowledge, and simplicity, is sufficient to discover credible explanatory hypotheses in a scientific task of current importance.
Optimal experimental design and some related control problems
, 2008
"... This paper traces the strong relations between experimental design and control, such as the use of optimal inputs to obtain precise parameter estimation in dynamical systems and the introduction of suitably designed perturbations in adaptive control. The mathematical background of optimal experiment ..."
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This paper traces the strong relations between experimental design and control, such as the use of optimal inputs to obtain precise parameter estimation in dynamical systems and the introduction of suitably designed perturbations in adaptive control. The mathematical background of optimal experimental design is briefly presented, and the role of experimental design in the asymptotic properties of estimators is emphasized. Although most of the paper concerns parametric models, some results are also presented for statistical learning and prediction with nonparametric models.
Adaptive design optimization: A mutual information based approach to model discrimination in cognitive science
 Neural Computation
, 2010
"... Discriminating among competing statistical models is a pressing issue for many experimentalists in the field of cognitive science. Resolving this issue begins with designing maximally informative experiments. To this end, the problem to be solved in adaptive design optimization is identifying experi ..."
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Cited by 5 (3 self)
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Discriminating among competing statistical models is a pressing issue for many experimentalists in the field of cognitive science. Resolving this issue begins with designing maximally informative experiments. To this end, the problem to be solved in adaptive design optimization is identifying experimental designs under which one can infer the underlying model in the fewest possible steps. When the models under consideration are nonlinear, as is often the case in cognitive science, this problem can be impossible to solve analytically without simplifying assumptions. However, as we show in this paper, a full solution can be found numerically with the help of a Bayesian computational trick derived from the statistics literature, which recasts the problem as a probability density simulation in which the optimal design is the mode of the density. We use a utility function based on mutual information, and give three intuitive interpretations of the utility function in terms of Bayesian posterior estimates. As a proof of concept, we offer a simple example application to an experiment on memory retention. 1
Coding under observation constraints
 in Proceedings of the Allerton Conference on Communication, Control, and Computing
, 2007
"... Abstract—We consider coding schemes when performance is measured by the average signal observation time to reliably decode an information bit, as opposed to conventional metrics of transmit energy per bit or spectral efficiency. This formulation is motivated by energy constrained communications devi ..."
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Abstract—We consider coding schemes when performance is measured by the average signal observation time to reliably decode an information bit, as opposed to conventional metrics of transmit energy per bit or spectral efficiency. This formulation is motivated by energy constrained communications devices where sampling the signal, rather than transmitting or processing it, dominates energy consumption. We show that sequentially observing samples with the maximum a posteriori entropy can significantly reduce observation costs. Equivalently, observation costs identical to traditional coding are achieved at blocklengths that are an order of magnitude smaller. To put this in perspective, our sampling strategy can be applied to realizing feedback systems that surpass the cutoff rate limit using the (24,12) Golay code, the highest such performance reported over the AWGN channel at these blocklengths. I.
N.: Selection of Perturbation Experiments for Model Discrimination
 Proceeddings of ECAI02, 2002
, 2000
"... Abstract. It often occurs that a system can be described by several competing models. In order to distinguish among the alternative models, further information about the behavior of the system is required. One way to obtain such information is to perform suitably chosen perturbation experiments. We ..."
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Abstract. It often occurs that a system can be described by several competing models. In order to distinguish among the alternative models, further information about the behavior of the system is required. One way to obtain such information is to perform suitably chosen perturbation experiments. We introduce a method for the selection of optimal perturbation experiments for discrimination among a set of dynamical models. The models are assumed to have the form of semiquantitative differential equations. The method employs an optimization criterion based on the entropy measure of information. 1
Model discrimination—another perspective on modelrobust designs
 Journal of Statistical Planning and Inference
, 2007
"... Recent progress in modelrobust designs has focused on maximiz1 ing estimation capacities. However, for a given design, two competing models may be both estimable and yet difficult or impossible to discriminate in the model selection procedure. In this paper, we propose several criteria for gaugin ..."
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Recent progress in modelrobust designs has focused on maximiz1 ing estimation capacities. However, for a given design, two competing models may be both estimable and yet difficult or impossible to discriminate in the model selection procedure. In this paper, we propose several criteria for gauging the capability of a design for model discrimination. The criteria are then used to evaluate a class of 18run orthogonal designs in terms of their modeldiscriminating capabilities. We demonstrate that designs having the same estimation capacity may differ considerably with respect to model discrimination capabilities. The best designs according to the proposed model discrimination criteria are obtained and tabulated for practical use.
Generating Information for RealTime Optimization
"... Realtime optimization (RTO) is an online optimization technique that monitors the behaviour of the process, looking for significant low frequency changes in the true plant optimum, while adjusting the setpoints of the process controllers to track these changes. The performance of the optimizer dep ..."
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Realtime optimization (RTO) is an online optimization technique that monitors the behaviour of the process, looking for significant low frequency changes in the true plant optimum, while adjusting the setpoints of the process controllers to track these changes. The performance of the optimizer depends on its ability to track these changes effectively and locate the true plant optimum operating conditions. By incorporating experimental design techniques, this thesis proposes an improvement to RTO performance by integrating information generation into the algorithm to reduce uncertainty in the final optimization results.