Mathematical modelling

This valuable source for graduate students and researchers provides a comprehensive introduction to current theories and applications in optimization methods and network models.

This book provides readers with a comprehensive and recent exposition in deep learning and its multidisciplinary applications, with a concentration on advances of deep learning architectures.

The focus of this thesis is the interplay of synchrony and adaptivity in complex networks.

This book discusses supply chain management, focusing on developments within modelling the dynamic behaviour of the supply chain.

This monograph deals with linear integra Volterra equations of the first kind with variable upper and lower limits of integration.

This book maximizes reader insights into the field of mathematical models and methods for the processing of two-dimensional remote sensing images.

This book presents a collection of papers emphasizing applications of mathematical models and methods to real-world problems of relevance for industry, life science, environment, finance and so on.

In this book, Dr.

This book provides an overview of different deep learning-based methods for face recognition and related problems.

This textbook presents a rigorous approach to multivariable calculus in the context of model building and optimization problems.

This textbook deals with engineering, science, technical, legal, financial, ICT, logistics and people management topics necessary for managing engineered assets such as all man-made tools, gadgets, buildings, equipment, machines, infrastructure, large-scale physical and industrial facilities and systems which pervade all sectors of industry.

This volume discusses the theoretical foundations of a new inter- and intra-disciplinary meta-research discipline, which can be succinctly called cognitive metamathematics, with the ultimate goal of achieving a global instance of concrete Artificial Mathematical Intelligence (AMI).

This book addresses the hazard of gas explosions in sealed underground coal mines, and how the risk of explosion can be assessed, modeled, and mitigated.

The concepts and techniques presented in this volume originated from the fields of dynamics, statistics, control theory, computer science and informatics, and are applied to novel and innovative real-world applications.

This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.

The computational models of physical systems comprise parameters, independent and dependent variables.

This textbook is an introduction to the subject of inverse problems with an emphasis on practical solution methods and applications from geophysics.

This work illustrates research conducted over a ten-year timespan and addresses a fundamental issue in reliability theory.

This book tackles the challenging question which mathematical formalisms and possibly new physical notions should be developed for quantitatively describing human cognition and behavior, in addition to the ones already developed in the physical and cognitive sciences.

This self-contained monograph presents the reader with an authoritative view of Continuous Optimization, an area of mathematical optimization that has experienced major developments during the past 40 years.

This book describes a novel methodology for studying algorithmic skills, intended as cognitive activities related to rule-based symbolic transformation, and argues that some human computational abilities may be interpreted and analyzed as genuine examples of extended cognition.

In Mathematical Finance, the authors consider a mathematical model for the pricing of emissions permits.

This book reports on advanced theories and methods in three related fields of research: applied physics, system science and computers.

This book covers topics in portfolio management and multicriteria decision analysis (MCDA), presenting a transparent and unified methodology for the portfolio construction process.

It seems doubtful whether we can expect to understand fully the instability of fluid flow without obtaining a mathematical representa- tion of the motion of a fluid in some particular case in which instability can actually be ob- served, so that a detailed comparison can be made between the results of analysis and those of experiment.

Machine learning and artificial intelligence (AI) are powerful tools that create predictive models, extract information, and help make complex decisions.

Reinforcement learning is a powerful tool in artificial intelligence in which virtual or physical agents learn to optimize their decision making to achieve long-term goals.

This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks.

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This book gathers peer-reviewed contributions submitted to the 21st European Conference on Mathematics for Industry, ECMI 2021, which was virtually held online, hosted by the University of Wuppertal, Germany, from April 13th to April 15th, 2021.

This book gathers contributions from the fourth edition of the Conference on "e;Philosophy and Theory of Artificial Intelligence"e; (PT-AI), held on 27-28th of September 2021 at Chalmers University of Technology, in Gothenburg, Sweden.

The investigation of the role of mechanical and mechano-chemical interactions in cellular processes and tissue development is a rapidly growing research field in the life sciences and in biomedical engineering.

This monograph is focused mostly on the exposition of analytical methods for the solution of problems of strong phase change.

This monograph is devoted to Eulerian models for fluid-structure interaction by applying the original point of view of level set methods.

This book collects a range of contributions on nonlinear dynamics and complexity, providing a systematic summary of recent developments, applications, and overall advances in nonlinearity, chaos, and complexity.

This contributed volume contains a collection of articles on state-of-the-art developments on the construction of theoretical integral techniques and their application to specific problems in science and engineering.

From its inception, artificial intelligence (AI) has had a rather ambivalent relationship with humans-swinging between their augmentation and replacement.

This book provides engineers and researchers knowledge to help them in system reliability analysis using machine learning, artificial intelligence, big data, genetic algorithm, information theory, multi-criteria decision making, and other techniques.

This book introduces a class of alignment models based on the so-called Cucker-Smale system as well as its kinetic and hydrodynamic counterparts.

This book comprises chapters authored by experts who are professors and researchers in internationally recognized universities and research institutions.

Learning to solve sequential decision-making tasks is difficult.

heterogeneous graphs.

Plan recognition, activity recognition, and goal recognition all involve making inferences about other actors based on observations of their interactions with the environment and other agents.

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry.