Data Driven Identification Of Networks Of Dynamic Systems
DOWNLOAD
Download Data Driven Identification Of Networks Of Dynamic Systems PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Data Driven Identification Of Networks Of Dynamic Systems book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Data Driven Identification Of Networks Of Dynamic Systems
DOWNLOAD
Author : Michel Verhaegen
language : en
Publisher: Cambridge University Press
Release Date : 2022-05-12
Data Driven Identification Of Networks Of Dynamic Systems written by Michel Verhaegen and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-12 with Mathematics categories.
A comprehensive introduction to identifying network-connected systems, covering models and methods, and applications in adaptive optics.
Data Driven Science And Engineering
DOWNLOAD
Author : Steven L. Brunton
language : en
Publisher: Cambridge University Press
Release Date : 2022-05-05
Data Driven Science And Engineering written by Steven L. Brunton and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-05 with Computers categories.
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Automating Data Driven Modelling Of Dynamical Systems
DOWNLOAD
Author : Dhruv Khandelwal
language : en
Publisher: Springer Nature
Release Date : 2022-02-03
Automating Data Driven Modelling Of Dynamical Systems written by Dhruv Khandelwal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-03 with Technology & Engineering categories.
This book describes a user-friendly, evolutionary algorithms-based framework for estimating data-driven models for a wide class of dynamical systems, including linear and nonlinear ones. The methodology addresses the problem of automating the process of estimating data-driven models from a user’s perspective. By combining elementary building blocks, it learns the dynamic relations governing the system from data, giving model estimates with various trade-offs, e.g. between complexity and accuracy. The evaluation of the method on a set of academic, benchmark and real-word problems is reported in detail. Overall, the book offers a state-of-the-art review on the problem of nonlinear model estimation and automated model selection for dynamical systems, reporting on a significant scientific advance that will pave the way to increasing automation in system identification.
Data Driven Identification Of Networks Of Dynamic Systems
DOWNLOAD
Author : Michel Verhaegen
language : en
Publisher:
Release Date : 2022
Data Driven Identification Of Networks Of Dynamic Systems written by Michel Verhaegen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with TECHNOLOGY & ENGINEERING categories.
"The identification of network connected dynamic systems is currently a hot research topic within the community of systems and control. Other engineering areas, social sciences and system biology are putting a lot of effort in the study of network connected systems. Modeling such networks and the identification of these models from acquired measurements is crucial in the analysis or understanding of the dynamics. Based on these models, synthesis to modify the behavior of the network can also be performed. This book gives a unique overview of state of the art research in the field of identifying networks of linear dynamical systems. This overview combines many of the pioneering contributions from the authors with those of other researchers that play a crucial role in the development of this new field"--
Dynamic Mode Decomposition
DOWNLOAD
Author : J. Nathan Kutz
language : en
Publisher: SIAM
Release Date : 2016-11-23
Dynamic Mode Decomposition written by J. Nathan Kutz and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-23 with Science categories.
Data-driven dynamical systems is a burgeoning field?it connects how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is a critically important new direction because the governing equations of many problems under consideration by practitioners in various scientific fields are not typically known. Thus, using data alone to help derive, in an optimal sense, the best dynamical system representation of a given application allows for important new insights. The recently developed dynamic mode decomposition (DMD) is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections with traditional dynamical systems theory and many recent innovations in compressed sensing and machine learning. Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems, the first book to address the DMD algorithm, presents a pedagogical and comprehensive approach to all aspects of DMD currently developed or under development; blends theoretical development, example codes, and applications to showcase the theory and its many innovations and uses; highlights the numerous innovations around the DMD algorithm and demonstrates its efficacy using example problems from engineering and the physical and biological sciences; and provides extensive MATLAB code, data for intuitive examples of key methods, and graphical presentations.
Data Driven Methods For Dynamic Systems
DOWNLOAD
Author : Jason Bramburger
language : en
Publisher: SIAM
Release Date : 2024-11-05
Data Driven Methods For Dynamic Systems written by Jason Bramburger and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-05 with Science categories.
As experimental data sets have grown and computational power has increased, new tools have been developed that have the power to model new systems and fundamentally alter how current systems are analyzed. This book brings together modern computational tools to provide an accurate understanding of dynamic data. The techniques build on pencil-and-paper mathematical techniques that go back decades and sometimes even centuries. The result is an introduction to state-of-the-art methods that complement, rather than replace, traditional analysis of time-dependent systems. Data-Driven Methods for Dynamic Systems provides readers with methods not found in other texts as well as novel ones developed just for this book; an example-driven presentation that provides background material and descriptions of methods without getting bogged down in technicalities; and examples that demonstrate the applicability of a method and introduce the features and drawbacks of their application. The online supplementary material includes a code repository that can be used to reproduce every example and that can be repurposed to fit a variety of applications not found in the book. This book is intended as an introduction to the field of data-driven methods for graduate students. It will also be of interest to researchers who want to familiarize themselves with the discipline. It can be used in courses on dynamical systems, differential equations, and data science.
Data Driven Modelling And Scientific Machine Learning In Continuum Physics
DOWNLOAD
Author : Krishna Garikipati
language : en
Publisher: Springer Nature
Release Date : 2024-07-29
Data Driven Modelling And Scientific Machine Learning In Continuum Physics written by Krishna Garikipati and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-29 with Mathematics categories.
This monograph takes the reader through recent advances in data-driven methods and machine learning for problems in science—specifically in continuum physics. It develops the foundations and details a number of scientific machine learning approaches to enrich current computational models of continuum physics, or to use the data generated by these models to infer more information on these problems. The perspective presented here is drawn from recent research by the author and collaborators. Applications drawn from the physics of materials or from biophysics illustrate each topic. Some elements of the theoretical background in continuum physics that are essential to address these applications are developed first. These chapters focus on nonlinear elasticity and mass transport, with particular attention directed at descriptions of phase separation. This is followed by a brief treatment of the finite element method, since it is the most widely used approach to solve coupled partial differential equations in continuum physics. With these foundations established, the treatment proceeds to a number of recent developments in data-driven methods and scientific machine learning in the context of the continuum physics of materials and biosystems. This part of the monograph begins by addressing numerical homogenization of microstructural response using feed-forward as well as convolutional neural networks. Next is surrogate optimization using multifidelity learning for problems of phase evolution. Graph theory bears many equivalences to partial differential equations in its properties of representation and avenues for analysis as well as reduced-order descriptions--all ideas that offer fruitful opportunities for exploration. Neural networks, by their capacity for representation of high-dimensional functions, are powerful for scale bridging in physics--an idea on which we present a particular perspective in the context of alloys. One of the most compelling ideas in scientific machine learning is the identification of governing equations from dynamical data--another topic that we explore from the viewpoint of partial differential equations encoding mechanisms. This is followed by an examination of approaches to replace traditional, discretization-based solvers of partial differential equations with deterministic and probabilistic neural networks that generalize across boundary value problems. The monograph closes with a brief outlook on current emerging ideas in scientific machine learning.
De Novo Quantum Cosmology With Artificial Intelligence
DOWNLOAD
Author : Ariel Fernández
language : en
Publisher: CRC Press
Release Date : 2025-07-22
De Novo Quantum Cosmology With Artificial Intelligence written by Ariel Fernández and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-22 with Science categories.
Experiments attempting to recreate the Big Bang and measurements in deep space point to the tantalizing possibility that our universe may be the relic of something simple, powerful, and highly symmetric. The evidence suggests an entity where matter and energy cannot be told apart and the four fundamental forces are unified into one. Empowered by artificial intelligence, De Novo Quantum Cosmology with Artificial Intelligence seeks to unravel the mystery as it searches for an encompassing physical picture where it all falls into place at the aftermath of creation from a quantum void. From the outset, AI reckons that the problem cannot be tackled without proper contextualization, that is, without dealing with other intimately related problems in particle cosmology including: the nature of dark matter and dark energy, the hierarchy problem of particle masses, the incommensurably weak coupling strength of gravity, the universe topology, the cosmological constant problem, and the vacuum catastrophe. Accordingly, the book addresses the matter in its full conceptual richness. This monograph addresses a broad readership that includes a nonhuman audience involving AI systems. A background in college-level physics and computer science would be essential. Although informal in the approach, the material is presented with scientific rigor, so that readers gain hands-on experience on the subject. The book is geared at graduate students as well as professional physicists, mathematicians, cosmologists, and big data scientists that seek to venture into some of the core problems in particle cosmology empowered by AI. Notably, the book is also geared at nonhuman audiences, since AI systems may incorporate its fundamental operational tenets and take the matter to unfathomable heights. Key Features: Introduces an artificial intelligence system to tackle core problems in particle cosmology Describes a grand unification scheme to explain the common origin of the fundamental forces Identifies the origin of matter as a phase transition from the quantum vacuum.
Dynamic Neural Networks For Robot Systems Data Driven And Model Based Applications
DOWNLOAD
Author : Long Jin
language : en
Publisher: Frontiers Media SA
Release Date : 2024-07-24
Dynamic Neural Networks For Robot Systems Data Driven And Model Based Applications written by Long Jin and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-24 with Science categories.
Neural network control has been a research hotspot in academic fields due to the strong ability of computation. One of its wildly applied fields is robotics. In recent years, plenty of researchers have devised different types of dynamic neural network (DNN) to address complex control issues in robotics fields in reality. Redundant manipulators are no doubt indispensable devices in industrial production. There are various works on the redundancy resolution of redundant manipulators in performing a given task with the manipulator model information known. However, it becomes knotty for researchers to precisely control redundant manipulators with unknown model to complete a cyclic-motion generation CMG task, to some extent. It is worthwhile to investigate the data-driven scheme and the corresponding novel dynamic neural network (DNN), which exploits learning and control simultaneously. Therefore, it is of great significance to further research the special control features and solve challenging issues to improve control performance from several perspectives, such as accuracy, robustness, and solving speed.
Artificial Intelligence
DOWNLOAD
Author : Utku Kose
language : en
Publisher: CRC Press
Release Date : 2024-11-29
Artificial Intelligence written by Utku Kose and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-29 with Computers categories.
This book provides an examination of cutting-edge research and developments in the field of artificial intelligence. It seeks to extend the view in both technical and societal evaluations to ensure a well-defined balance for societal outcomes. It explores hot topics such as generative artificial intelligence, artificial intelligence in law, education, and climate change. Artificial Intelligence: Technical and Societal Advancements seeks to bridge the gap between theory and practical applications of AI by giving readers insight into recent advancements. It offers readers a deep dive into the transformative power of AI for the present and future world. As artificial intelligence continues to revolutionize various sectors, the book discusses applications from healthcare to finance and from entertainment to industrial areas. It discusses the technical aspects of intelligent systems and the effects of these aspects on humans. To this point, this book considers technical advancements while discussing the societal pros and cons in terms of human-machine interaction in critical applications. The authors also stress the importance of deriving policies and predictions about how to make future intelligent systems compatible with humans through a necessary level of human management. Finally, this book provides the opinions and views of researchers and experts (from public/private sector) including educators, lawyers, policymakers, managers, and business-related representatives. The target readers of this book include academicians; researchers; experts; policymakers; educators; and B.S., M.S., and Ph.D. students in the context of target problem fields. It can be used accordingly as a reference source and even supportive material for artificial intelligence-oriented courses.