To construct a continuous D-dimensional function that representssparse data, we use either a single function depending on dmax can reliably be recovered (or only coupling up to a certain order can be recovered if orig...
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ISBN:
(纸本)9783642149405
To construct a continuous D-dimensional function that representssparse data, we use either a single function depending on dmax can reliably be recovered (or only coupling up to a certain order can be recovered if original coordinates are used). This justifies using functions of lower dimensionality. We use functions of d
The need for reduced models of chemical kinetics is motivated by the fact that the simulation of reactive flows with detailed chemistry is generally computationally expensive. In dissipative dynamical systems differen...
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ISBN:
(纸本)9783642149405
The need for reduced models of chemical kinetics is motivated by the fact that the simulation of reactive flows with detailed chemistry is generally computationally expensive. In dissipative dynamical systems different time scales cause an anisotropically contracting phase flow. Most kinetic modelreduction approaches explicitly exploit this and separate the dynamics into fast and slow modes. We propose an implicit approach for the approximation of slow attracting manifolds by computing trajectories as solutions of an optimization problem suggesting a variational principle characterizing trajectories near slow attracting manifolds. The objective functional for the identification of suitable trajectories is supposed to represent the extent of relaxation of chemical forces along the trajectories which is proposed to be minimal on the slow manifold. Corresponding geometric criteria are motivated via fundamental concepts from differential geometry and physics. They are compared to each other through three kinetic reaction mechanisms.
models which involve the coupling of complex chemical and physical processes are being increasingly used within engineering design and decision making. Improvements in available compute power have allowed us to repres...
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ISBN:
(纸本)9783642149405
models which involve the coupling of complex chemical and physical processes are being increasingly used within engineering design and decision making. Improvements in available compute power have allowed us to represent such processes with increasing levels of model detail. However, our ability to accurately specify the required high dimensional input data often does not keep pace with the development of model structure. The analysis ofmodel uncertainty must therefore form a key part of the evaluation of such models. Furthermore, sensitivity analysis methods, which determine the parameters contributing most to output uncertainty, can inform the process of model improvement. In this paper we show by example that global sensitivity methods, and in particular methods based onquasi-random sampling high dimensional model representation (QRS-HDMR), are capable of contributing to the model evaluation and improvement process by highlighting key parameters and model subcomponents which drive the output uncertainty of complex models. The method of QRS-HDMR will be described and its application within the fields of combustion and reactive pollution dispersion will be demonstrated. The key points addressed in the work are (1) the potential for complexityreduction using QRS-HDMR methods, (2) global vs. local sensitivity indices for exploring the response to parameters in complex non-linear models, (3) the possibilities for parameter tuning or feasible set reduction via comparison of models with experiment whilst incorporating uncertainty/sensitivity analysis, (4) model improvement through parameter importance ranking coupled with further ab initio modelling studies, (5) robustness to model structure. The generation of a meta-model via QRS-HDMR is shown to be a reasonably efficient global sensitivity method for systems where effects are limited to second-order. Where higher order effects exist, simple transformations of model outputs are shown to improve the accuracy of the met
The solution to the problem of finding the reaction kinetic realization of a given system obeying the mass action law containing the minimal/maximal number of reactions and complexes is shown in this paper. The propos...
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ISBN:
(纸本)9783642149405
The solution to the problem of finding the reaction kinetic realization of a given system obeying the mass action law containing the minimal/maximal number of reactions and complexes is shown in this paper. The proposed methods are based on Mixed Integer Linear Programming where the mass action kinetics is encoded into the linear constraints. Although the problems are NP-hard in the current setting, the developed algorithms give a usable answer to some of the questions first raised in [1].
We present novel results on a non-integrable generalized KdV equation proposed by Fokas [A.S. Fokas, Physica D87, 145 (1995)], aiming to describe unidirectional solitary water waves with greater accuracy than the stan...
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ISBN:
(纸本)9783642149405
We present novel results on a non-integrable generalized KdV equation proposed by Fokas [A.S. Fokas, Physica D87, 145 (1995)], aiming to describe unidirectional solitary water waves with greater accuracy than the standard KdV equation. The profile of the solitary wave solutions is determined via a reduction of the partial differential equation (PDE) to a set of ordinary differential equations (ODEs). Subsequently, we study the stability of the wave using this profile as initial condition for the PDE. In the case of the standard KdV equation it is well-known that the solitary wave solutions are always stable, irrespective of their height. However, in the case of our higher-order KdV equation we find that the stability of the solutions breaks down beyond a certain critical height, just like solitary waves in real water experiments.
The purpose of this paper is twofold: (1) To provide a conciseaut] Constantinos Theodoropoulos review of methods, recently presented in the literature, which have developed and/or used modelreduction technologies for...
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ISBN:
(纸本)9783642149405
The purpose of this paper is twofold: (1) To provide a conciseaut] Constantinos Theodoropoulos review of methods, recently presented in the literature, which have developed and/or used modelreduction technologies for the optimisation and control of large-scale linear and nonlinear systems and (2) to present an overview of the collection of related technologies that have been developed within our group at the University of Manchester concerning the modelreduction-based steady-state and dynamic optimisation of large-scale systems, modelled with black-box dynamic and steady state solvers. Furthermore, a new methodology for the linear model predictive control of large-scale non-linear systems will be presented. It relies on adaptive linearisations of the discretised state-space equations using low-order projections of the system's gradients. Thetubular reactor has been used as an illustrative example to demonstrate the capabilities of all the above methods due to its high nonlinearity, exhibited through a number of bifurcations at different parameter combinations, and distributed parameter characteristics.
This isaut]Grigory L. Litvinovaut]Victor P. Maslovaut]Anatoly Ya. Rodionovaut]Andrei N. Sobolevskii a survey paper on applications of mathematics of semirings to numerical analysis and computing. Concepts of universal...
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ISBN:
(纸本)9783642149405
This isaut]Grigory L. Litvinovaut]Victor P. Maslovaut]Anatoly Ya. Rodionovaut]Andrei N. Sobolevskii a survey paper on applications of mathematics of semirings to numerical analysis and computing. Concepts of universal algorithm and generic program are discussed. Relations between these concepts and mathematics of semirings are examined. A very brief introduction to mathematics of semirings (including idempotent and tropical mathematics) is presented. Concrete applications to optimization problems, idempotent linear algebra and interval analysis are indicated. It is known that some nonlinear problems (and especially optimization problems) become linear over appropriate semirings with idempotent addition (the so-called idempotent superposition principle). This linearity over semirings is convenient for parallel computations.
The biomedical signals are often corrupted by noise in their acquisition or transmission resulting in lower Signal to Noise Ratio (SNR), which brings problematic obstacles to successive biomedical signal processing. S...
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The biomedical signals are often corrupted by noise in their acquisition or transmission resulting in lower Signal to Noise Ratio (SNR), which brings problematic obstacles to successive biomedical signal processing. So suppressing noise and improving SNR effectively is an essential procedure and key issue in the research on biomedical signal processing. In this paper, we propose a novel multi-model fast denoising method based on the Wavelet transform threshold denoising. The proposed denoising scheme not only solves the Pseudo-Gibbs phenomenon to filter the signal effectively but also preserves the signal details to retain the diagnostic information. Meanwhile, the summed data processing method is advanced to realize the fast denoising. The simulation experiments on electrocardiogram(ECG) indicate that the proposed method can effectively and quickly separate signal from noise.
We discuss synchronization in networks of Hindmarsh-Rose neurons that are interconnected via gap junctions, also known as electrical synapses. We present theoretical results for interactions without time-delay. These ...
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We discuss synchronization in networks of Hindmarsh-Rose neurons that are interconnected via gap junctions, also known as electrical synapses. We present theoretical results for interactions without time-delay. These results are supported by experiments with a setup consisting of sixteen electronic equivalents of the Hindmarsh-Rose neuron. We show experimental results of networks where time-delay on the interaction is taken into account. We discuss in particular the influence of the network topology on the synchronization.
We present several applications of non-linear datamodeling, using principal manifolds and principal graphs constructed using the metaphor of elasticity (elastic principal graph approach). These approaches are general...
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We present several applications of non-linear datamodeling, using principal manifolds and principal graphs constructed using the metaphor of elasticity (elastic principal graph approach). These approaches are generalizations of the Kohonen's self-organizing maps, a class of artificial neural networks. On several examples we show advantages of using non-linear objects for data approximation in comparison to the linear ones. We propose four numerical criteria for comparing linear and non-linear mappings of datasets into the spaces of lower dimension. The examples are taken from comparative political science, from analysis of high-throughput data in molecular biology, from analysis of dynamical systems.
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