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Mathematical models are at the core of modern science and technology. An accurate description of behaviors, systems and processes often requires the use of complex models which are difficult to analyze and control. To facilitate analysis of and design for complex systems, model reduction theory and tools allow determining "simpler" models which preserve some of the features of the underlying complex description. A large variety of techniques, which can be distinguished depending on the features which are preserved in the reduction process, has been proposed to achieve this goal. One such a method is the moment matching approach. This monograph focuses on the problem of model reduction by moment matching for nonlinear systems. The central idea of the method is the preservation, for a prescribed class of inputs and under some technical assumptions, of the steady-state output response of the system to be reduced. We present the moment matching approach from this vantage point, covering the problems of model reduction for nonlinear systems, nonlinear time-delay systems, data-driven model reduction for nonlinear systems and model reduction for "discontinuous" input signals. Throughout the monograph linear systems, with their simple structure and strong properties, are used as a paradigm to facilitate understanding of the theory and provide foundation of the terminology and notation. The text is enriched by several numerical examples, physically motivated examples and with connections to well-established notions and tools, such as the phasor transform.

Nonlinear systems --- Mathematical models.

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The Second-Order Adjoint Sensitivity Analysis Methodology generalizes the First-Order Theory presented in the author’s previous books published by CRC Press. This breakthrough has many applications in sensitivity and uncertainty analysis, optimization, data assimilation, model calibration, and reducing uncertainties in model predictions. The book has many illustrative examples that will help readers understand the complexity of the subject and will enable them to apply this methodology to problems in their own fields. Highlights: • Covers a wide range of needs, from graduate students to advanced researchers • Provides a text positioned to be the primary reference for high-order sensitivity and uncertainty analysis • Applies to all fields involving numerical modeling, optimization, quantification of sensitivities in direct and inverse problems in the presence of uncertainties. About the Author: Dan Gabriel Cacuci is a South Carolina SmartState Endowed Chair Professor and the Director of the Center for Nuclear Science and Energy, Department of Mechanical Engineering at the University of South Carolina. He has a Ph.D. in Applied Physics, Mechanical and Nuclear Engineering from Columbia University. He is also the recipient of many awards including four honorary doctorates, the Ernest Orlando Lawrence Memorial award from the U.S. Dept. of Energy and the Arthur Holly Compton, Eugene P. Wigner and the Glenn Seaborg Awards from the American Nuclear Society.

Sensitivity theory (Mathematics) --- Large scale systems. --- Nonlinear systems.

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Stability --- Nonlinear systems --- Mathematical models. --- Systems, Nonlinear --- System theory --- Dynamics --- Mechanics --- Motion --- Vibration --- Benjamin-Feir instability --- Equilibrium

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"Computational Methods for Nonlinear Dynamical Systems proposes novel ideas and develops highly efficient and accurate methods for solving nonlinear dynamical systems, drawing inspiration from the weighted residual method and the asymptomatic method. The book also introduces global estimation methods and local computational methods for nonlinear dynamical systems. Starting from the classic asymptomatic, finite difference and weighted residual methods, typical methods for solving nonlinear dynamical systems are considered. All proposed are new high-performance methods, such as time-domain collocation and local variational iteration. These proposed methods can be used both for real-time simulation and for the analysis of nonlinear dynamics in aerospace engineering. This book summarizes and develops computational methods for strongly nonlinear dynamical systems and considers the practical application of the methods within aerospace engineering, making it an essential resource for those working in this area."--

Aerospace engineering --- Differentiable dynamical systems. --- Nonlinear systems. --- Systems, Nonlinear --- System theory --- Differential dynamical systems --- Dynamical systems, Differentiable --- Dynamics, Differentiable --- Differential equations --- Global analysis (Mathematics) --- Topological dynamics

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This book collects recent developments in nonlinear and complex systems. It provides up-to-date theoretic developments and new techniques based on a nonlinear dynamical systems approach that can be used to model and understand complex behavior in nonlinear dynamical systems. It covers symmetry groups, conservation laws, risk reduction management, barriers in Hamiltonian systems, and synchronization and chaotic transient. Illustrating mathematical modeling applications to nonlinear physics and nonlinear engineering, the book is ideal for academic and industrial researchers concerned with machinery and controls, manufacturing, and controls. · Introduces new concepts for understanding and modeling complex systems; · Explains risk reduction management in complex systems; · Examines the symmetry group approach to understanding complex systems; · Illustrates the relation between transient chaos and crises.

Nonlinear systems. --- Engineering. --- Vibration. --- Complexity. --- Applications of Nonlinear Dynamics and Chaos Theory. --- Mathematical Modeling and Industrial Mathematics. --- Vibration, Dynamical Systems, Control. --- Cycles --- Mechanics --- Sound --- Construction --- Industrial arts --- Technology --- Computational complexity. --- Statistical physics. --- Mathematical models. --- Dynamical systems. --- Dynamics. --- Dynamical systems --- Kinetics --- Mathematics --- Mechanics, Analytic --- Force and energy --- Physics --- Statics --- Models, Mathematical --- Simulation methods --- Mathematical statistics --- Complexity, Computational --- Electronic data processing --- Machine theory --- Statistical methods