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The understanding of complex systems is a key element to predict and control the system’s dynamics. To gain deeper insights into the underlying actions of complex systems today, more and more data of diverse types are analyzed that mirror the systems dynamics, whereas system models are still hard to derive. Data assimilation merges both data and model to an optimal description of complex systems’ dynamics. The present eBook brings together both recent theoretical work in data assimilation and control and demonstrates applications in diverse research fields.
Book Synopsis Data Assimilation and Control: Theory and Applications in Life Sciences by : Axel Hutt
Download or read book Data Assimilation and Control: Theory and Applications in Life Sciences written by Axel Hutt and published by Frontiers Media SA. This book was released on 2019-08-16 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: The understanding of complex systems is a key element to predict and control the system’s dynamics. To gain deeper insights into the underlying actions of complex systems today, more and more data of diverse types are analyzed that mirror the systems dynamics, whereas system models are still hard to derive. Data assimilation merges both data and model to an optimal description of complex systems’ dynamics. The present eBook brings together both recent theoretical work in data assimilation and control and demonstrates applications in diverse research fields.
This monograph addresses the state of the art of reduced order methods for modeling and computational reduction of complex parametrized systems, governed by ordinary and/or partial differential equations, with a special emphasis on real time computing techniques and applications in computational mechanics, bioengineering and computer graphics. Several topics are covered, including: design, optimization, and control theory in real-time with applications in engineering; data assimilation, geometry registration, and parameter estimation with special attention to real-time computing in biomedical engineering and computational physics; real-time visualization of physics-based simulations in computer science; the treatment of high-dimensional problems in state space, physical space, or parameter space; the interactions between different model reduction and dimensionality reduction approaches; the development of general error estimation frameworks which take into account both model and discretization effects. This book is primarily addressed to computational scientists interested in computational reduction techniques for large scale differential problems.
Book Synopsis Reduced Order Methods for Modeling and Computational Reduction by : Alfio Quarteroni
Download or read book Reduced Order Methods for Modeling and Computational Reduction written by Alfio Quarteroni and published by Springer. This book was released on 2014-06-05 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph addresses the state of the art of reduced order methods for modeling and computational reduction of complex parametrized systems, governed by ordinary and/or partial differential equations, with a special emphasis on real time computing techniques and applications in computational mechanics, bioengineering and computer graphics. Several topics are covered, including: design, optimization, and control theory in real-time with applications in engineering; data assimilation, geometry registration, and parameter estimation with special attention to real-time computing in biomedical engineering and computational physics; real-time visualization of physics-based simulations in computer science; the treatment of high-dimensional problems in state space, physical space, or parameter space; the interactions between different model reduction and dimensionality reduction approaches; the development of general error estimation frameworks which take into account both model and discretization effects. This book is primarily addressed to computational scientists interested in computational reduction techniques for large scale differential problems.
Numerical Control: Part B, Volume 24 in the Handbook of Numerical Analysis series, highlights new advances in the field, with this new volume presenting interesting chapters written by an international board of authors. Chapters in this volume include Control problems in the coefficients and the domain for linear elliptic equations, Computational approaches for extremal geometric eigenvalue problems, Non-overlapping domain decomposition in space and time for PDE-constrained optimal control problems on networks, Feedback Control of Time-dependent Nonlinear PDEs with Applications in Fluid Dynamics, Stabilization of the Navier-Stokes equations - Theoretical and numerical aspects, Reconstruction algorithms based on Carleman estimates, and more. Other sections cover Discrete time formulations as time discretization strategies in data assimilation, Back and forth iterations/Time reversal methods, Unbalanced Optimal Transport: from Theory to Numerics, An ADMM Approach to the Exact and Approximate Controllability of Parabolic Equations, Nonlocal balance laws -- an overview over recent results, Numerics and control of conservation laws, Numerical approaches for simulation and control of superconducting quantum circuits, and much more. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Handbook of Numerical Analysis series Updated release includes the latest information on Numerical Control
Book Synopsis Numerical Control: Part B by : Emmanuel Trélat
Download or read book Numerical Control: Part B written by Emmanuel Trélat and published by Elsevier. This book was released on 2023-02-20 with total page 662 pages. Available in PDF, EPUB and Kindle. Book excerpt: Numerical Control: Part B, Volume 24 in the Handbook of Numerical Analysis series, highlights new advances in the field, with this new volume presenting interesting chapters written by an international board of authors. Chapters in this volume include Control problems in the coefficients and the domain for linear elliptic equations, Computational approaches for extremal geometric eigenvalue problems, Non-overlapping domain decomposition in space and time for PDE-constrained optimal control problems on networks, Feedback Control of Time-dependent Nonlinear PDEs with Applications in Fluid Dynamics, Stabilization of the Navier-Stokes equations - Theoretical and numerical aspects, Reconstruction algorithms based on Carleman estimates, and more. Other sections cover Discrete time formulations as time discretization strategies in data assimilation, Back and forth iterations/Time reversal methods, Unbalanced Optimal Transport: from Theory to Numerics, An ADMM Approach to the Exact and Approximate Controllability of Parabolic Equations, Nonlocal balance laws -- an overview over recent results, Numerics and control of conservation laws, Numerical approaches for simulation and control of superconducting quantum circuits, and much more. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Handbook of Numerical Analysis series Updated release includes the latest information on Numerical Control
This book is about the dynamics of neural systems and should be suitable for those with a background in mathematics, physics, or engineering who want to see how their knowledge and skill sets can be applied in a neurobiological context. No prior knowledge of neuroscience is assumed, nor is advanced understanding of all aspects of applied mathematics! Rather, models and methods are introduced in the context of a typical neural phenomenon and a narrative developed that will allow the reader to test their understanding by tackling a set of mathematical problems at the end of each chapter. The emphasis is on mathematical- as opposed to computational-neuroscience, though stresses calculation above theorem and proof. The book presents necessary mathematical material in a digestible and compact form when required for specific topics. The book has nine chapters, progressing from the cell to the tissue, and an extensive set of references. It includes Markov chain models for ions, differential equations for single neuron models, idealised phenomenological models, phase oscillator networks, spiking networks, and integro-differential equations for large scale brain activity, with delays and stochasticity thrown in for good measure. One common methodological element that arises throughout the book is the use of techniques from nonsmooth dynamical systems to form tractable models and make explicit progress in calculating solutions for rhythmic neural behaviour, synchrony, waves, patterns, and their stability. This book was written for those with an interest in applied mathematics seeking to expand their horizons to cover the dynamics of neural systems. It is suitable for a Masters level course or for postgraduate researchers starting in the field of mathematical neuroscience.
Book Synopsis Neurodynamics by : Stephen Coombes
Download or read book Neurodynamics written by Stephen Coombes and published by Springer Nature. This book was released on 2023-05-09 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about the dynamics of neural systems and should be suitable for those with a background in mathematics, physics, or engineering who want to see how their knowledge and skill sets can be applied in a neurobiological context. No prior knowledge of neuroscience is assumed, nor is advanced understanding of all aspects of applied mathematics! Rather, models and methods are introduced in the context of a typical neural phenomenon and a narrative developed that will allow the reader to test their understanding by tackling a set of mathematical problems at the end of each chapter. The emphasis is on mathematical- as opposed to computational-neuroscience, though stresses calculation above theorem and proof. The book presents necessary mathematical material in a digestible and compact form when required for specific topics. The book has nine chapters, progressing from the cell to the tissue, and an extensive set of references. It includes Markov chain models for ions, differential equations for single neuron models, idealised phenomenological models, phase oscillator networks, spiking networks, and integro-differential equations for large scale brain activity, with delays and stochasticity thrown in for good measure. One common methodological element that arises throughout the book is the use of techniques from nonsmooth dynamical systems to form tractable models and make explicit progress in calculating solutions for rhythmic neural behaviour, synchrony, waves, patterns, and their stability. This book was written for those with an interest in applied mathematics seeking to expand their horizons to cover the dynamics of neural systems. It is suitable for a Masters level course or for postgraduate researchers starting in the field of mathematical neuroscience.
Data assimilation is an approach that combines observations and model output, with the objective of improving the latter. This book places data assimilation into the broader context of inverse problems and the theory, methods, and algorithms that are used for their solution. It provides a framework for, and insight into, the inverse problem nature of data assimilation, emphasizing ?why? and not just ?how.? Methods and diagnostics are emphasized, enabling readers to readily apply them to their own field of study. Readers will find a comprehensive guide that is accessible to nonexperts; numerous examples and diverse applications from a broad range of domains, including geophysics and geophysical flows, environmental acoustics, medical imaging, mechanical and biomedical engineering, economics and finance, and traffic control and urban planning; and the latest methods for advanced data assimilation, combining variational and statistical approaches.
Book Synopsis Data Assimilation: Methods, Algorithms, and Applications by : Mark Asch
Download or read book Data Assimilation: Methods, Algorithms, and Applications written by Mark Asch and published by SIAM. This book was released on 2016-12-29 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data assimilation is an approach that combines observations and model output, with the objective of improving the latter. This book places data assimilation into the broader context of inverse problems and the theory, methods, and algorithms that are used for their solution. It provides a framework for, and insight into, the inverse problem nature of data assimilation, emphasizing ?why? and not just ?how.? Methods and diagnostics are emphasized, enabling readers to readily apply them to their own field of study. Readers will find a comprehensive guide that is accessible to nonexperts; numerous examples and diverse applications from a broad range of domains, including geophysics and geophysical flows, environmental acoustics, medical imaging, mechanical and biomedical engineering, economics and finance, and traffic control and urban planning; and the latest methods for advanced data assimilation, combining variational and statistical approaches.
Book Synopsis Dynamic Data Assimilation by : John M. Lewis
Download or read book Dynamic Data Assimilation written by John M. Lewis and published by Cambridge University Press. This book was released on 2006-08-03 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher description
A survey of how engineering techniques from control and systems theory can be used to help biologists understand the behavior of cellular systems.
Book Synopsis Control Theory and Systems Biology by : Pablo A. Iglesias
Download or read book Control Theory and Systems Biology written by Pablo A. Iglesias and published by MIT Press. This book was released on 2010 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: A survey of how engineering techniques from control and systems theory can be used to help biologists understand the behavior of cellular systems.
This book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including targeting observation, sensitivity analysis, and parameter estimation. The book will be useful to individual researchers as well as graduate students for a reference in the field of data assimilation.
Book Synopsis Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II) by : Seon Ki Park
Download or read book Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II) written by Seon Ki Park and published by Springer Science & Business Media. This book was released on 2013-05-22 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including targeting observation, sensitivity analysis, and parameter estimation. The book will be useful to individual researchers as well as graduate students for a reference in the field of data assimilation.
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Book Synopsis Data-Driven Science and Engineering by : Steven L. Brunton
Download or read book Data-Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Today's world poses a triple threat to the American population: infectious diseases, contamination of food and water, and bioattacks (biowarfare or bioterrorism). At least 17 countries are producing weapons of mass destruction using viruses, bacteria, or their toxins. AIDS, E. coli contamination, drug-resistant tuberculosis, and virulent flu strains are perhaps the best known of a host of disease threats. What these dangers have in common is the amount of data required to achieve solutions; in some cases, as much as a petabit (1 followed by 15 zeros) of data is required to study large numbers of samples from widespread locations. Firepower in the Lab examines how the nation can combat this triple threat by improving our ability to detect, measure, and monitor harmful biological agents. It explores the potential of today's exciting new laboratory automation and computer technologies as well as the emerging tools of molecular biology-how we can generate and analyze more data quickly and reduce human hands-on involvement, which inevitably introduces errors. The book discusses how to improve and apply technologies such as robotics, laboratory automation, "lab-on-a-chip," bioinformatics, and Internet control innovations. It reviews lessons learned from our experience with pandemic flu viruses. It also presents strategies for developing new high-throughput technologies, including how to address the lack of public funding for critical research undertakings.
Book Synopsis Firepower in the Lab by : N. Patel
Download or read book Firepower in the Lab written by N. Patel and published by Joseph Henry Press. This book was released on 2001-07-01 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today's world poses a triple threat to the American population: infectious diseases, contamination of food and water, and bioattacks (biowarfare or bioterrorism). At least 17 countries are producing weapons of mass destruction using viruses, bacteria, or their toxins. AIDS, E. coli contamination, drug-resistant tuberculosis, and virulent flu strains are perhaps the best known of a host of disease threats. What these dangers have in common is the amount of data required to achieve solutions; in some cases, as much as a petabit (1 followed by 15 zeros) of data is required to study large numbers of samples from widespread locations. Firepower in the Lab examines how the nation can combat this triple threat by improving our ability to detect, measure, and monitor harmful biological agents. It explores the potential of today's exciting new laboratory automation and computer technologies as well as the emerging tools of molecular biology-how we can generate and analyze more data quickly and reduce human hands-on involvement, which inevitably introduces errors. The book discusses how to improve and apply technologies such as robotics, laboratory automation, "lab-on-a-chip," bioinformatics, and Internet control innovations. It reviews lessons learned from our experience with pandemic flu viruses. It also presents strategies for developing new high-throughput technologies, including how to address the lack of public funding for critical research undertakings.
