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Author: Lorenz Biegler Publisher: John Wiley & Sons ISBN: 1119957583 Category : Mathematics Languages : en Pages : 403
Book Description
This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman filtering methods and optimization-based approaches to solving inverse problems. The aim is to cross-fertilize the perspectives of researchers in the areas of data assimilation, statistics, large-scale optimization, applied and computational mathematics, high performance computing, and cutting-edge applications. The solution to large-scale inverse problems critically depends on methods to reduce computational cost. Recent research approaches tackle this challenge in a variety of different ways. Many of the computational frameworks highlighted in this book build upon state-of-the-art methods for simulation of the forward problem, such as, fast Partial Differential Equation (PDE) solvers, reduced-order models and emulators of the forward problem, stochastic spectral approximations, and ensemble-based approximations, as well as exploiting the machinery for large-scale deterministic optimization through adjoint and other sensitivity analysis methods. Key Features: Brings together the perspectives of researchers in areas of inverse problems and data assimilation. Assesses the current state-of-the-art and identify needs and opportunities for future research. Focuses on the computational methods used to analyze and simulate inverse problems. Written by leading experts of inverse problems and uncertainty quantification. Graduate students and researchers working in statistics, mathematics and engineering will benefit from this book.
Author: Lorenz Biegler Publisher: John Wiley & Sons ISBN: 1119957583 Category : Mathematics Languages : en Pages : 403
Book Description
This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman filtering methods and optimization-based approaches to solving inverse problems. The aim is to cross-fertilize the perspectives of researchers in the areas of data assimilation, statistics, large-scale optimization, applied and computational mathematics, high performance computing, and cutting-edge applications. The solution to large-scale inverse problems critically depends on methods to reduce computational cost. Recent research approaches tackle this challenge in a variety of different ways. Many of the computational frameworks highlighted in this book build upon state-of-the-art methods for simulation of the forward problem, such as, fast Partial Differential Equation (PDE) solvers, reduced-order models and emulators of the forward problem, stochastic spectral approximations, and ensemble-based approximations, as well as exploiting the machinery for large-scale deterministic optimization through adjoint and other sensitivity analysis methods. Key Features: Brings together the perspectives of researchers in areas of inverse problems and data assimilation. Assesses the current state-of-the-art and identify needs and opportunities for future research. Focuses on the computational methods used to analyze and simulate inverse problems. Written by leading experts of inverse problems and uncertainty quantification. Graduate students and researchers working in statistics, mathematics and engineering will benefit from this book.
Author: Athanasios C. Antoulas Publisher: SIAM ISBN: 9780898718713 Category : Mathematics Languages : en Pages : 504
Book Description
Mathematical models are used to simulate, and sometimes control, the behavior of physical and artificial processes such as the weather and very large-scale integration (VLSI) circuits. The increasing need for accuracy has led to the development of highly complex models. However, in the presence of limited computational, accuracy, and storage capabilities, model reduction (system approximation) is often necessary. Approximation of Large-Scale Dynamical Systems provides a comprehensive picture of model reduction, combining system theory with numerical linear algebra and computational considerations. It addresses the issue of model reduction and the resulting trade-offs between accuracy and complexity. Special attention is given to numerical aspects, simulation questions, and practical applications. Audience: anyone interested in model reduction, including graduate students and researchers in the fields of system and control theory, numerical analysis, and the theory of partial differential equations/computational fluid dynamics.
Author: Alexander N. Gorban Publisher: Springer Science & Business Media ISBN: 3642149413 Category : Mathematics Languages : en Pages : 356
Book Description
This volume contains the extended version of selected talks given at the international research workshop "Coping with Complexity: Model Reduction and Data Analysis", Ambleside, UK, August 31 – September 4, 2009. The book is deliberately broad in scope and aims at promoting new ideas and methodological perspectives. The topics of the chapters range from theoretical analysis of complex and multiscale mathematical models to applications in e.g., fluid dynamics and chemical kinetics.
Author: Javad Mohammadpour Publisher: Springer Science & Business Media ISBN: 144195757X Category : Technology & Engineering Languages : en Pages : 350
Book Description
Complexity and dynamic order of controlled engineering systems is constantly increasing. Complex large scale systems (where "large" reflects the system’s order and not necessarily its physical size) appear in many engineering fields, such as micro-electromechanics, manufacturing, aerospace, civil engineering and power engineering. Modeling of these systems often result in very high-order models imposing great challenges to the analysis, design and control problems. "Efficient Modeling and Control of Large-Scale Systems" compiles state-of-the-art contributions on recent analytical and computational methods for addressing model reduction, performance analysis and feedback control design for such systems. Also addressed at length are new theoretical developments, novel computational approaches and illustrative applications to various fields, along with: - An interdisciplinary focus emphasizing methods and approaches that can be commonly applied in various engineering fields -Examinations of applications in various fields including micro-electromechanical systems (MEMS), manufacturing processes, power networks, traffic control "Efficient Modeling and Control of Large-Scale Systems" is an ideal volume for engineers and researchers working in the fields of control and dynamic systems.
Author: Andrew P. Featherstone Publisher: Springer Science & Business Media ISBN: 1447104137 Category : Science Languages : en Pages : 177
Book Description
Sheet and film processes include coating, papermaking, metal rolling, and polymer film extrusion. Products produced by these processes include paper, bumper stickers, plastic bags, windshield safety glass, and sheet metal. The total capitalization of industries that rely on these processes is well over $ 500 billion worldwide. These processes are notorious for being difficult to control. The goal of this book is to present the theoretical background and practical techniques for the identification and control of sheet and film processes. It is explained why many existing industrial control systems perform poorly for sheet and film processes. Identification and control algorithms are described and illustrated which provide consistent and reliable product quality. These algorithms include an experimental design technique that ensures that informative data are collected during input-output experimentation, model identification techniques that produce a process model and an estimate of its accuracy, and control techniques that take into account actuator constraints as well as robustness to model uncertainties. The algorithms covered in this book are truly the state of the art. Variations on some of the algorithms have been implemented on industrial sheet and film processes. Other algorithms are in various stages of implementation. All of the algorithms have been applied to realistic simulation models constructed from industrial plant data; many of these studies are included in this book.
Author: Athanasios C. Antoulas Publisher: SIAM ISBN: 0898716586 Category : Mathematics Languages : en Pages : 489
Book Description
Mathematical models are used to simulate, and sometimes control, the behavior of physical and artificial processes such as the weather and very large-scale integration (VLSI) circuits. The increasing need for accuracy has led to the development of highly complex models. However, in the presence of limited computational accuracy and storage capabilities model reduction (system approximation) is often necessary. Approximation of Large-Scale Dynamical Systems provides a comprehensive picture of model reduction, combining system theory with numerical linear algebra and computational considerations. It addresses the issue of model reduction and the resulting trade-offs between accuracy and complexity. Special attention is given to numerical aspects, simulation questions, and practical applications.
Author: Valentina E. Balas Publisher: Springer Nature ISBN: 9464630744 Category : Computers Languages : en Pages : 299
Book Description
This is an open access book. The focus of the conference is to provide a unique platform for exchange of ideas and synergy among researchers, academicians and industrial experts across the globe belonging to emerging electrical engineering domains. It also provides a premier platform for the people to present and discuss the most recent innovations and solutions in solving complex and challenging problems related to intelligent electrical engineering systems. Such a blend of various research-oriented minds will lead to productive results and further advancements in electrical engineering research. The book invites submission of novel, recent area of innovation and previously unpublished research work/idea in the field of modern applications of artificial intelligence techniques to electrical engineering systems. The applications of artificial intelligence related to various fields of electrical engineering are mentioned in the conference tracks. The conference is meant to discuss the challenges and applications of latest evolutionary computing techniques, neural networks, fuzzy logic, machine learning and data analytics in the fields of power systems, power electronics, robotics, automation, instrumentation, control systems, mechatronics and photonics. It provides a platform to the students, researchers, scientists, faculty members, professionals and practitioners to interact, present and get innovative ideas in the field of electrical engineering. As a part of AITEES-2022, many keynote sessions are planned to enhance the research and innovation skills of participants. Eminent professors from academic institutions and world renowned industrial experts from India and abroad will deliver keynote sessions.
Author: Christopher Beattie Publisher: Springer Nature ISBN: 303095157X Category : Science Languages : en Pages : 462
Book Description
This book celebrates Professor Thanos Antoulas's 70th birthday, marking his fundamental contributions to systems and control theory, especially model reduction and, more recently, data-driven modeling and system identification. Model reduction is a prominent research topic with wide ranging scientific and engineering applications.
Author: A. C. Antoulas Publisher: SIAM ISBN: 1611976081 Category : Mathematics Languages : en Pages : 245
Book Description
Dynamical systems are a principal tool in the modeling, prediction, and control of a wide range of complex phenomena. As the need for improved accuracy leads to larger and more complex dynamical systems, direct simulation often becomes the only available strategy for accurate prediction or control, inevitably creating a considerable burden on computational resources. This is the main context where one considers model reduction, seeking to replace large systems of coupled differential and algebraic equations that constitute high fidelity system models with substantially fewer equations that are crafted to control the loss of fidelity that order reduction may induce in the system response. Interpolatory methods are among the most widely used model reduction techniques, and Interpolatory Methods for Model Reduction is the first comprehensive analysis of this approach available in a single, extensive resource. It introduces state-of-the-art methods reflecting significant developments over the past two decades, covering both classical projection frameworks for model reduction and data-driven, nonintrusive frameworks. This textbook is appropriate for a wide audience of engineers and other scientists working in the general areas of large-scale dynamical systems and data-driven modeling of dynamics.
Author: Peter Benner Publisher: Springer Nature ISBN: 3030729834 Category : Mathematics Languages : en Pages : 415
Book Description
This contributed volume presents some of the latest research related to model order reduction of complex dynamical systems with a focus on time-dependent problems. Chapters are written by leading researchers and users of model order reduction techniques and are based on presentations given at the 2019 edition of the workshop series Model Reduction of Complex Dynamical Systems – MODRED, held at the University of Graz in Austria. The topics considered can be divided into five categories: system-theoretic methods, such as balanced truncation, Hankel norm approximation, and reduced-basis methods; data-driven methods, including Loewner matrix and pencil-based approaches, dynamic mode decomposition, and kernel-based methods; surrogate modeling for design and optimization, with special emphasis on control and data assimilation; model reduction methods in applications, such as control and network systems, computational electromagnetics, structural mechanics, and fluid dynamics; and model order reduction software packages and benchmarks. This volume will be an ideal resource for graduate students and researchers in all areas of model reduction, as well as those working in applied mathematics and theoretical informatics.
Author: Lorenz Biegler
Author: Lorenz Biegler
Author: Athanasios C. Antoulas
Author: Alexander N. Gorban
Author: Javad Mohammadpour
Author: Andrew P. Featherstone
Author: Athanasios C. Antoulas
Author: Valentina E. Balas
Author: Christopher Beattie
Author: A. C. Antoulas
Author: Peter Benner