Probabilistic Data Driven Modeling
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Probabilistic Data Driven Modeling
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Author : Tomaso Aste
language : en
Publisher: Cambridge University Press
Release Date : 2025-05-01
Probabilistic Data Driven Modeling written by Tomaso Aste 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 2025-05-01 with Computers categories.
A probabilistic data-driven modeling toolbox to help students and researchers characterize, classify and model real complex systems.
Coastal Storms
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Author : Paolo Ciavola
language : en
Publisher: John Wiley & Sons
Release Date : 2017-03-31
Coastal Storms written by Paolo Ciavola and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-31 with Science categories.
A comprehensive introduction to coastal storms and their associated impacts Coastal Storms offers students and professionals in the field a comprehensive overview and groundbreaking text that is specifically devoted to the analysis of coastal storms. Based on the most recent knowledge and contributions from leading researchers, the text examines coastal storms’ processes and characteristics, the main hazards (such as overwash, inundation and flooding, erosion, structures overtopping), and how to monitor and model storms. The authors include information on the most advanced innovations in forecasting, prediction, and early warning, which serves as a foundation for accurate risk evaluation and developing adequate coastal indicators and management options. In addition, structural overtopping and damage are explained, taking into account the involved hydrodynamic and morphodynamic processes. The monitoring methods of coastal storms are analyzed based on recent results from research projects in Europe and the United States. Methods for vulnerability and risk evaluation are detailed, storm impact indicators are suggested for different hazards and coastal management procedures analyzed. This important resource includes: Comprehensive coverage of storms and associated impacts, including meteorological coastal storm definitions and related potential consequences A state-of-the-art reference for advanced students, professionals and researchers in the field Chapters on monitoring methods of coastal storms, their prediction, early warning systems, and modeling of consequences Explorations of methods for vulnerability and risk evaluation and suggestions for storm impact indicators for different hazards and coastal management procedures Coastal Storms is a compilation of scientific and policy-related knowledge related to climate-related extreme events. The authors are internationally recognized experts and their work reflects the most recent science and policy advances in the field.
Data Driven Computational Neuroscience
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Author : Concha Bielza
language : en
Publisher: Cambridge University Press
Release Date : 2020-11-26
Data Driven Computational Neuroscience written by Concha Bielza 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 2020-11-26 with Computers categories.
Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.
Computational Intelligence Applications In Business Intelligence And Big Data Analytics
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Author : Vijayan Sugumaran
language : en
Publisher: CRC Press
Release Date : 2017-06-26
Computational Intelligence Applications In Business Intelligence And Big Data Analytics written by Vijayan Sugumaran and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-26 with Computers categories.
There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive mechanisms. This book sets its focus on CI based architectures, modeling, case studies and applications in big data analytics, and business intelligence. The intended audiences of this book are scientists, professionals, researchers, and academicians who deal with the new challenges and advances in the specific areas mentioned above. Designers and developers of applications in these areas can learn from other experts and colleagues through this book.
Machine Learning And Probabilistic Graphical Models For Decision Support Systems
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Author : Kim Phuc Tran
language : en
Publisher: CRC Press
Release Date : 2022-10-13
Machine Learning And Probabilistic Graphical Models For Decision Support Systems written by Kim Phuc Tran and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-13 with Computers categories.
This book presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multicriteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their uses in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter.
Practical Hydroinformatics
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Author : Robert J. Abrahart
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-10-24
Practical Hydroinformatics written by Robert J. Abrahart and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-24 with Science categories.
Hydroinformatics is an emerging subject that is expected to gather speed, momentum and critical mass throughout the forthcoming decades of the 21st century. This book provides a broad account of numerous advances in that field - a rapidly developing discipline covering the application of information and communication technologies, modelling and computational intelligence in aquatic environments. A systematic survey, classified according to the methods used (neural networks, fuzzy logic and evolutionary optimization, in particular) is offered, together with illustrated practical applications for solving various water-related issues. ...
Machine Learning
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Author : Tony Jebara
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Machine Learning written by Tony Jebara and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Machine Learning: Discriminative and Generative covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines. However, unlike previous books that only discuss these rather different approaches in isolation, it bridges the two schools of thought together within a common framework, elegantly connecting their various theories and making one common big-picture. Also, this bridge brings forth new hybrid discriminative-generative tools that combine the strengths of both camps. This book serves multiple purposes as well. The framework acts as a scientific breakthrough, fusing the areas of generative and discriminative learning and will be of interest to many researchers. However, as a conceptual breakthrough, this common framework unifies many previously unrelated tools and techniques and makes them understandable to a larger portion of the public. This gives the more practical-minded engineer, student and the industrial public an easy-access and more sensible road map into the world of machine learning. Machine Learning: Discriminative and Generative is designed for an audience composed of researchers & practitioners in industry and academia. The book is also suitable as a secondary text for graduate-level students in computer science and engineering.
Handbook Of Dynamic Data Driven Applications Systems
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Author : Frederica Darema
language : en
Publisher: Springer Nature
Release Date : 2023-09-14
Handbook Of Dynamic Data Driven Applications Systems written by Frederica Darema and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-14 with Computers categories.
This Second Volume in the series Handbook of Dynamic Data Driven Applications Systems (DDDAS) expands the scope of the methods and the application areas presented in the first Volume and aims to provide additional and extended content of the increasing set of science and engineering advances for new capabilities enabled through DDDAS. The methods and examples of breakthroughs presented in the book series capture the DDDAS paradigm and its scientific and technological impact and benefits. The DDDAS paradigm and the ensuing DDDAS-based frameworks for systems’ analysis and design have been shown to engender new and advanced capabilities for understanding, analysis, and management of engineered, natural, and societal systems (“applications systems”), and for the commensurate wide set of scientific and engineering fields and applications, as well as foundational areas. The DDDAS book series aims to be a reference source of many of the important research and development efforts conducted under the rubric of DDDAS, and to also inspire the broader communities of researchers and developers about the potential in their respective areas of interest, of the application and the exploitation of the DDDAS paradigm and the ensuing frameworks, through the examples and case studies presented, either within their own field or other fields of study. As in the first volume, the chapters in this book reflect research work conducted over the years starting in the 1990’s to the present. Here, the theory and application content are considered for: Foundational Methods Materials Systems Structural Systems Energy Systems Environmental Systems: Domain Assessment & Adverse Conditions/Wildfires Surveillance Systems Space Awareness Systems Healthcare Systems Decision Support Systems Cyber Security Systems Design of Computer Systems The readers of this book series will benefit from DDDAS theory advances such as object estimation, information fusion, and sensor management. The increased interest in Artificial Intelligence (AI), Machine Learning and Neural Networks (NN) provides opportunities for DDDAS-based methods to show the key role DDDAS plays in enabling AI capabilities; address challenges that ML-alone does not, and also show how ML in combination with DDDAS-based methods can deliver the advanced capabilities sought; likewise, infusion of DDDAS-like approaches in NN-methods strengthens such methods. Moreover, the “DDDAS-based Digital Twin” or “Dynamic Digital Twin”, goes beyond the traditional DT notion where the model and the physical system are viewed side-by-side in a static way, to a paradigm where the model dynamically interacts with the physical system through its instrumentation, (per the DDDAS feed-back control loop between model and instrumentation).
Data Driven Models In Inverse Problems
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Author : Tatiana A. Bubba
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2024-11-18
Data Driven Models In Inverse Problems written by Tatiana A. Bubba and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-18 with Mathematics categories.
Advances in learning-based methods are revolutionizing several fields in applied mathematics, including inverse problems, resulting in a major paradigm shift towards data-driven approaches. This volume, which is inspired by this cutting-edge area of research, brings together contributors from the inverse problem community and shows how to successfully combine model- and data-driven approaches to gain insight into practical and theoretical issues.
Machine Learning Under Resource Constraints Applications
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Author : Katharina Morik
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2022-12-31
Machine Learning Under Resource Constraints Applications written by Katharina Morik and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-31 with Science categories.
Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 3 describes how the resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples. In the areas of health and medicine, it is demonstrated how machine learning can improve risk modelling, diagnosis, and treatment selection for diseases. Machine learning supported quality control during the manufacturing process in a factory allows to reduce material and energy cost and save testing times is shown by the diverse real-time applications in electronics and steel production as well as milling. Additional application examples show, how machine-learning can make traffic, logistics and smart cities more effi cient and sustainable. Finally, mobile communications can benefi t substantially from machine learning, for example by uncovering hidden characteristics of the wireless channel.