Matematisk modellering

This book is a selection of the journal publications of William T Ziemba. Over a span of 50 years, Professor Ziemba contributed to a wide variety of disciplines including Stochastic Programming, Portfolio Theory, Sports Betting, and Risk Management. In his work he collaborated with academics and practitioners worldwide. Bill wrote for a variety of audiences. He was widely known as a leading practitioner of operations research methods applied to problems in financial planning and sports betting.Prior to his death, Bill Ziemba was working on a multivolume series on his collected papers. The Selected Works of William T Ziemba: A Memorial Volume captures some of the sentiment of Professor Ziemba's plans.

This graduate textbook provides a self-contained introduction to the classical theory of partial differential equations (PDEs). Through its careful selection of topics and engaging tone, readers will also learn how PDEs connect to cutting-edge research and the modeling of physical phenomena. The scope of the Third Edition greatly expands on that of the previous editions by including five new chapters covering additional PDE topics relevant for current areas of active research. This includes coverage of linear parabolic equations with measurable coefficients, parabolic DeGiorgi classes, Navier-Stokes equations, and more. The ¿Problems and Complements¿ sections have also been updated to feature new exercises, examples, and hints toward solutions, making this a timely resource for students.Partial Differential Equations: Third Edition is ideal for graduate students interested in exploring the theory of PDEs and how they connect to contemporary research. It can also serve as a useful tool for more experienced readers who are looking for a comprehensive reference.

This book presents and surveys normalization techniques with a deep analysis in training deep neural networks.  In addition, the author provides technical details in designing new normalization methods and network architectures tailored to specific tasks.  Normalization methods can improve the training stability, optimization efficiency, and generalization ability of deep neural networks (DNNs) and have become basic components in most state-of-the-art DNN architectures.  The author provides guidelines for elaborating, understanding, and applying normalization methods.  This book is ideal for readers working on the development of novel deep learning algorithms and/or their applications to solve practical problems in computer vision and machine learning tasks.  The book also serves as a resource researchers, engineers, and students who are new to the field and need to understand and train DNNs.

"Dispersed multiphase flows are frequently found in nature and have diverse geophysical, environmental, industrial, and energy applications. This book targets a beginning graduate student looking to learn about the physical processes that govern these flows, going from the fundamentals to the state of the art, with many exercises included"--

Dieses Buch ist Teil eines 6-bändigen Werks. Es befasst sich mit der Anwendung von Differentialgleichungen in diversen Bereichen der Physik und dem Ingenieurwesen. Dabei wird die Bilanzgleichung ins Zentrum der Betrachtung gerückt. Die Lesenden lernen Schritt für Schritt, wie ein konkret gestelltes Problem mit Hilfe sinnvoller Voraussetzungen und Idealisierungen modelliert, als Bilanz formuliert und formalisiert und die entstandene Differentialgleichung bzw. das entstandene Differentialgleichungssystem exakt oder numerisch gelöst wird. Dieses didaktische Konzept wird durchgehend sorgfältig und konsequent für jedes Teilgebiet angewandt und ermöglicht auf diese Weise den Studierenden die Bilanz als etwas Grundlegendes zur Beschreibung eines physikalischen Sachverhalts zu begreifen. Sämtliche theoretisch gewonnenen Ergebnisse werden in Worten festgehalten, teilweise mit Hilfe von Computersimulationen dargestellt und von vielen, konkreten, vollständig gelösten Beispielen begleitet.

Dieses Buch ist Teil eines 6-bändigen Werks. Es ist als Wegweiser für den Einstieg in die Modellierung von Populationen und deren Interaktion gedacht. Die Lesenden erfahren in nachvollziehbaren Schritten, wie ein Ökosystem unter sinnvollen Annahmen und Idealisierungen mit Hilfe von Differentialgleichungen modelliert werden kann und wie die bestehenden Modelle bei einem Eingriff des Menschen oder aufgrund der Berücksichtigung von Phänomenen wie etwa intra- und interspezifische Konkurrenz oder Begrenzung des Nahrungsangebots usw. angepasst werden müssen. Die dabei entstandene Differentialgleichung bzw. das entstandene Differentialgleichungssystem wird exakt oder numerisch gelöst, die Ergebnisse in Worten und mit Hilfe von Computersimulationen festgehalten und von vielen, konkreten, vollständig gelösten Beispielen begleitet.

Dieses 6-bändige Werk befasst sich mit der Anwendung von Differentialgleichungen in diversen Bereichen der Physik und dem Ingenieurwesen. Dabei wird die Bilanzgleichung ins Zentrum der Betrachtung gerückt. Die Lesenden lernen Schritt für Schritt, wie ein konkret gestelltes Problem mit Hilfe sinnvoller Voraussetzungen und Idealisierungen modelliert, als Bilanz formuliert und formalisiert und die entstandene Differentialgleichung bzw. das entstandene Differentialgleichungssystem exakt oder numerisch gelöst wird. Dieses didaktische Konzept wird durchgehend sorgfältig und konsequent für jedes Teilgebiet angewandt und ermöglicht auf diese Weise den Studierenden die Bilanz als etwas Grundlegendes zur Beschreibung eines physikalischen Sachverhalts zu begreifen. Sämtliche theoretisch gewonnenen Ergebnisse werden in Worten festgehalten und von vielen, konkreten, vollständig gelösten Beispielen begleitet.

This contributed volume investigates several mathematical techniques for the modeling and simulation of viral pandemics, with a special focus on COVID-19.  Modeling a pandemic requires an interdisciplinary approach with other fields such as epidemiology, virology, immunology, and biology in general.  Spatial dynamics and interactions are also important features to be considered, and a multiscale framework is needed at the level of individuals and the level of virus particles and the immune system.  Chapters in this volume address these items, as well as offer perspectives for the future.

Learn to write algorithms and program in the new field of quantum computing. This second edition is updated to equip you with the latest knowledge and tools needed to be a complex problem-solver in this ever-evolving landscape. The book has expanded its coverage of current and future advancements and investments by IT companies in this emerging technology. Most chapters are thoroughly revised to incorporate the latest updates to IBM Quantum's systems and offerings, such as improved algorithms, integrating hardware advancements, software enhancements, bug fixes, and more. Yoüll examine quantum computing in the cloud and run experiments there on a real quantum device. Along the way yoüll cover game theory with the Magic Square, an example of quantum pseudo-telepathy. Yoüll also learn to write code using QISKit, Python SDK, and other APIs such as QASM and execute it against simulators (local or remote) or a real quantum computer. Then peek inside the inner workings of the Bell states for entanglement, Grover¿s algorithm for linear search, Shor¿s algorithm for integer factorization, and other algorithms in the fields of optimization, and more. Finally, yoüll learn the current quantum algorithms for entanglement, random number generation, linear search, integer factorization, and others. By the end of this book, yoüll understand how quantum computing provides massive parallelism and significant computational speedups over classical computersWhat You'll LearnWrite algorithms that provide superior performance over their classical counterpartsCreate a quantum number generator: the quintessential coin flip with a quantum twistExamine the quantum algorithms in use today for random number generation, linear search, and moreDiscover quantum teleportationHandle the counterfeit coin problem, a classic puzzle Put your knowledge to the testwith more than 150 practice exercises Who This Book Is ForDevelopers, programmers, computer science researchers, teachers, and students.

Mithilfe dieses kompakten Buchs wird ein erstes strukturiertes Verständnis fu¿r die mathematischen Grundlagen digitaler Bilder und deren weitere Bearbeitung vermittelt. Ziel des Buchs ist es Interesse zu wecken und eine Basis zu geben um sich in Folge vertiefend mit digitaler und mathematischer Bildbearbeitung auseinander setzen zu können. Als alleinstehendes Werk ist es geeignet einen ersten Einblick in die Hintergru¿nde der mittlerweile alltäglichen Bearbeitung von digitalen Bildern zu bekommen. Fu¿r das Verständnis der Inhalte ist ein elementares Wissen aus Linearer Algebra von Vorteil. Alle besprochenen Themen werden mathematisch motiviert und visuell dargestellt.

This monograph is devoted to Eulerian models for fluid-structure interaction by applying the original point of view of level set methods.In the last 15 years, Eulerian models have become popular tools for studying fluid-structure interaction problems. One major advantage compared to more conventional methods such as ALE methods is that they allow the use of a single grid and a single discretization method for the different media. Level set methods in addition provide a general framework to follow the fluid-solid interfaces, to represent the elastic stresses of solids, and to model the contact forces between solids.This book offers a combination of mathematical modeling, aspects of numerical analysis, elementary codes and numerical illustrations, providing the reader with insights into the applications and performance of these models.Assuming background at the level of a Master's degree, Level Set Methods for Fluid-Structure Interaction provides researchers in the fields of numerical analysis of PDEs, theoretical and computational mechanics with a basic reference on the topic. Its pedagogical style and organization make it particularly suitable for graduate students and young researchers.

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. It will also be useful to students learning reliability engineering.The book brings readers up to date with how system reliability relates to the latest techniques of AI, big data, genetic algorithm, information theory, and multi-criteria decision making and points toward future developments in the subject.

This book describes different mathematical modeling and soft computing techniques used to solve practical engineering problems. It gives an overview of the current state of soft computing techniques and describes the advantages and disadvantages of soft computing compared to traditional hard computing techniques. Through examples and case studies, the editors demonstrate and describe how problems with inherent uncertainty can be addressed and eventually solved through the aid of numerical models and methods. The chapters address several applications and examples in bioengineering science, drug delivery, solving inventory issues, Industry 4.0, augmented reality and weather forecasting. Other examples include solving fuzzy-shortest-path problems by introducing a new distance and ranking functions. Because, in practice, problems arise with uncertain data and most of them cannot be solved exactly and easily, the main objective is to develop models that deliver solutions with the aid of numerical methods. This is the reason behind investigating soft numerical computing in dynamic systems. Having this in mind, the authors and editors have considered error of approximation and have discussed several common types of errors and their propagations. Moreover, they have explained the numerical methods, along with convergence and consistence properties and characteristics, as the main objectives behind this book involve considering, discussing and proving related theorems within the setting of soft computing. This book examines dynamic models, and how time is fundamental to the structure of the model and data as well as the understanding of how a process unfolds * Discusses mathematical modeling with soft computing and the implementations of uncertain mathematical models * Examines how uncertain dynamic systems models include uncertain state, uncertain state space and uncertain state's transition functions * Assists readers to become familiar with many soft numerical methods to simulate the solution function's behaviorThis book is intended for system specialists who are interested in dynamic systems that operate at different time scales. The book can be used by engineering students, researchers and professionals in control and finite element fields as well as all engineering, applied mathematics, economics and computer science interested in dynamic and uncertain systems.Ali Ahmadian is a Senior Lecturer at the Institute of IR 4.0, The National University of Malaysia.Soheil Salahshour is an associate professor at Bahcesehir University.

This book summarizes new discoveries on fracturing in chalk. Based on studies on the Danish North Sea, this book shows how observations from outcrop analogues, core and seismic data can be used to characterize the density, distribution and geometry of natural fractures in chalk and marl. Laboratory experiments on chalk samples reveal the controls on the geomechanical properties of chalk and thus on the growth of natural fractures. Finally, various modeling techniques are employed to investigate the mechanical deformation in the chalk structures of the Danish North Sea and to predict fracture distribution and geometry in the subsurface. An understanding of fracture density, distribution and geometry is essential for planning efficient fluid extraction or injection strategies and CO2 sequestration. This book provides the necessary knowledge.

Many nonlinear systems around us can generate a very complex and counter-intuitive dynamics that contrasts with their simplicity, but their understanding requires concepts that are outside the basic training of most science students. This textbook, which is the fruit of graduate courses that the authors have taught at their respective universities, provides a richly illustrated introduction to nonlinear dynamical systems and chaos and a solid foundation for this fascinating subject. It will satisfy those who want discover this field, including at the undergraduate level, but also those who need a compact and consistent overview, gathering the concepts essential to nonlinear scientists.The first and second chapters describe the essential concepts needed to describe nonlinear dynamical systems as well as their stability. The third chapter introduces the concept of bifurcation, where the qualitative dynamical behavior of a system changes. The fourth chapter deals with oscillations, from their birth to their destabilization, and how they respond to external driving. The fifth and sixth chapters discuss complex behaviors that only occur in state spaces of dimension three and higher: quasi-periodicity and chaos, from their general properties to quantitative methods of characterization. All chapters are supplemented by exercises ranging from direct applications of the notions introduced in the corresponding chapter to elaborate problems involving concepts from different chapters, as well as numerical explorations.

This volume collects papers based on plenary and invited talks given at the 50th Barrett Memorial Lectures on Approximation, Applications, and Analysis of Nonlocal, Nonlinear Models that was organized by the University of Tennessee, Knoxville and held virtually in May 2021. The three-day meeting brought together experts from the computational, scientific, engineering, and mathematical communities who work with nonlocal models. These proceedings collect contributions and give a survey of the state of the art in computational practices, mathematical analysis, applications of nonlocal models, and explorations of new application domains. The volume benefits from the mixture of contributions by computational scientists, mathematicians, and application specialists. The content is suitable for graduate students as well as specialists working with nonlocal models and covers topics on fractional PDEs, regularity theory for kinetic equations, approximation theory for fractional diffusion, analysis of nonlocal diffusion model as a bridge between local and fractional PDEs, and more.

This book presents high-quality research papers presented at 2nd International Conference on Smart Data Intelligence (ICSMDI 2022) organized by Kongunadu College of Engineering and Technology at Trichy, Tamil Nadu, India, during April 2022. This book brings out the new advances and research results in the fields of algorithmic design, data analysis, and implementation on various real-time applications. It discusses many emerging related fields like big data, data science, artificial intelligence, machine learning, and deep learning which have deployed a paradigm shift in various data-driven approaches that tends to evolve new data-driven research opportunities in various influential domains like social networks, healthcare, information, and communication applications.

Lisa Schneider entwickelt und implementiert ein Untersuchungsinstrument zur Analyse von Lösungsprozessen beim mathematischen Modellieren, das Modelling-Activity-Interaction-Tool (MAI-Tool). Das MAI-Tool zeichnet sich durch eine digitale Erfassung sowie eine automatisierte Darstellung und algorithmische Auswertung der Daten aus. Im Rahmen einer empirischen Studie wird das MAI-Tool angewendet, in der Modellierungsprozesse von Lernenden beim Lösen einer mathematischen Modellierungsaufgabe untersucht werden: Die Hälfte der Lernenden erhielt eine Unterweisung in idealtypische Modellierungsprozesse, die andere Hälfte nicht. Der Einfluss der Unterweisung auf die Struktur individueller Lösungsprozesse kann nachgewiesen werden und daraus ableitend Hürden während des Modellierens identifiziert werden. Außerdem werden Ergebnisse hinsichtlich des Zusammenhangs von Interaktionen und der Gruppenstruktur präsentiert. Die in der Studie erhobenen Daten werden zur Entwicklung eines Algorithmus mitMachine Learning verwendet, der individuelle Lösungsprozesse anhand von Strukturmerkmalen klassifiziert. Insgesamt leistet diese Forschungsarbeit einen Beitrag zur Grundlagenforschung in mathematischer Modellierung auf technologischer, empirischer und algorithmischer Ebene.

This book comprises chapters authored by experts who are professors and researchers in internationally recognized universities and research institutions. The book presents the results of research and descriptions of real-world systems, services, and technologies. Reading this book, researchers, professional practitioners, and graduate students will gain a clear vision on the state of the art of the research and real-world practice on system dependability and analytics.The book is published in honor of Professor Ravishankar K. Iyer, the George and Ann Fisher Distinguished Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC), Urbana, Illinois. Professor Iyer is ACM Fellow, IEEE Fellow, AAAS Fellow, and served as Interim Vice Chancellor of UIUC for research during 2008-2011. The book contains chapters written by many of his former students.

This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book¿s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.

This book discusses computational methods related to biological models using mathematical tools and techniques. The book chapters concentrate on numerical and analytical techniques that provide a global solution for biological models while keeping long-term benefits in mind. The solutions are useful in closely understanding biological models, and the results will be very useful for mathematicians, engineers, doctors, scientists and researchers working on real-life biological models. This book provides significant and current knowledge of biological models related to real-life applications. The book covers both methods and applications.

Founded in 1985 by Jean-Claude Falmagne and Jean-Paul Doignon, Knowledge Structure Theory (KST) constitutes a rigorous and current mathematical theory for the representation and the assessment of human knowledge. The seminal work of these authors initiated a highly active research strand with an ever-growing literature, mostly scattered across various technical journals.Starting from a concise but comprehensive introduction to its foundations, this volume provides a state-of-the-art review of KST. For the first time the volume brings together the most important theoretical developments and extensions of the last decade and presents new areas of application beyond education, with contributions by key researchers in the field.Among the important advances covered by this book are (1) a comprehensive treatment of probabilistic models in KST; (2) polytomous extensions of the theory; (3) KST-based psychological diagnostics and neuropsychological assessment; (4) the representation and assessment of cognitive skills in problem solving, as well as procedural skills. In addition, this book also includes an overview of available software for the application of KST.

This book collects the results presented at the 158th European Study Group with Industry, which took place at the Centre de Recerca Matemàtica in Barcelona in January 2020. The European Study Groups with Industry are a well-recognised forum where mathematicians work with industrial representatives on issues of current interest to companies. At this particular meeting, the problems were chosen to provide a wide variety of subject areas and to appeal to local academics. In this work, the research carried out and the solutions presented to the companies are detailed. In particular, the book focuses on: estimating the difficulty level of mobile games; modelling the stability of human towers; fibre coating in the manufacture of clutch components; safe trajectories of robot arms. The book provides an excellent addition to the canon of Industrial Mathematics. It is addressed to researchers keen to apply mathematics to topical, real-world problems.

This book focuses on contemporary technologies and research in computational intelligence that has reached the practical level and is now accessible in preclinical and clinical settings. This book's principal objective is to thoroughly understand significant technological breakthroughs and research results in predictive modeling in healthcare imaging and data analysis. Machine learning and deep learning could be used to fully automate the diagnosis and prognosis of patients in medical fields. The healthcare industry's emphasis has evolved from a clinical-centric to a patient-centric model. However, it is still facing several technical, computational, and ethical challenges. Big data analytics in health care is becoming a revolution in technical as well as societal well-being viewpoints.  Moreover, in this age of big data, there is increased access to massive amounts of regularly gathered data from the healthcare industry that has necessitated the development of predictive models and automated solutions for the early identification of critical and chronic illnesses. The book contains high-quality, original work that will assist readers in realizing novel applications and contexts for deep learning architectures and algorithms, making it an indispensable reference guide for academic researchers, professionals, industrial software engineers, and innovative model developers in healthcare industry.

This volume highlights the latest advances, innovations, and applications in the field of sustainable concrete structures, as presented by scientists and engineers at the RILEM International Conference on Numerical Modeling Strategies for Sustainable Concrete Structures (SSCS), held in Marseille, France, on July 4-6, 2022. It demonstrates that numerical methods (finite elements, finite volumes, finite differences) are a relevant response to the challenge to optimize the utilization of cement in concrete constructions while checking that these constructions have a lifespan compatible with the stakes of sustainable development. They are indeed accurate tools for an optimized design of concrete constructions, and allow us to consider all types of complexities: for example, those linked to rheological, physicochemical and mechanical properties of concrete, those linked to the geometry of the structures or even to the environmental boundary conditions. This optimization must also respect constraints of time, money, security, energy, CO2 emissions, and, more generally, life cycle more reliably than the codes and analytical approaches currently used. Numerical methods are, undoubtedly, the best calculation tools at the service of concrete eco-construction. The contributions present traditional and new ideas that will open novel research directions and foster multidisciplinary collaboration between different specialists.

The NUMISHEET conference series is the most significant international conference on the area of the numerical simulation of sheet metal forming processes. It gathers the most prominent experts in numerical methods in sheet forming processes and is an outstanding forum for the exchange of ideas and for the discussion of technologies related to sheet metal forming processes. Topics covered in this volume include but are not limited to the following: Materials Modeling and Experimental Testing MethodsFriction and ContactFormability, Necking, and FractureInstabilities and Surface DefectsFracture and DamageNumerical MethodsSpringbackIncremental Sheet FormingRoll FormingInnovative Forming MethodsProduct and Process Design and Optimization

This book presents the proceedings of the 19th International Conference of the Indian Society of Ergonomics (HWWE), held in Guwahati, India, on December 1-3, 2021. By highlighting the latest theories and models, as well as cutting-edge technologies and applications, and by combining findings from a range of disciplines including engineering, design, health care, management, computer science, and behavioral science, it provides researchers and practitioners alike with a comprehensive, timely guide on user-centered design for quality life, human factors and ergonomics, design applications, cognitive processing, and response. It also offers an excellent source of innovative ideas to stimulate future discussions and developments aimed at applying knowledge and techniques to optimize system performance, while at the same time promoting the health, safety, and well-being of individuals. The proceedings includes papers from researchers and practitioners, scientists and physicians, institutional leaders, managers, and policy makers that contribute to constructing the human factors and ergonomics approach across a variety of methodologies, domains, and productive sectors.

This book contains reports made at the International Conference on Differential Equations, Mathematical Modeling and Computational Algorithms, held in Belgorod, Russia, in October 2021 and is devoted to various aspects of the theory of differential equations and their applications in various branches of science. Theoretical papers devoted to the qualitative analysis of emerging mathematical objects, theorems of the existence and uniqueness of solutions to the boundary value problems under study are presented, and numerical algorithms for their solution are described. Some issues of mathematical modeling are also covered; in particular, in problems of economics, computational aspects of the theory of differential equations and boundary value problems are studied. The articles are written by well-known experts and are interesting and useful to a wide audience: mathematicians, representatives of applied sciences and students and postgraduates of universities engaged in applied mathematics.