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Author: Debasish Roy
Publisher: Cambridge University Press
Published: 2017-05-04
Total Pages: 749
ISBN-13: 1107182646
DOWNLOAD EBOOK →This book introduces essential concepts in stochastic processes that interface seamlessly with applications of interest in science and engineering.
Author: Debasish Roy
Publisher: Cambridge University Press
Published: 2017-05-04
Total Pages: 750
ISBN-13: 1316996174
DOWNLOAD EBOOK →Targeted at graduate students, researchers and practitioners in the field of science and engineering, this book gives a self-contained introduction to a measure-theoretic framework in laying out the definitions and basic concepts of random variables and stochastic diffusion processes. It then continues to weave into a framework of several practical tools and applications involving stochastic dynamical systems. These include tools for the numerical integration of such dynamical systems, nonlinear stochastic filtering and generalized Bayesian update theories for solving inverse problems and a new stochastic search technique for treating a broad class of non-convex optimization problems. MATLAB® codes for all the applications are uploaded on the companion website.
Author: George Yin
Publisher: Springer Nature
Published: 2022-04-22
Total Pages: 466
ISBN-13: 3030985199
DOWNLOAD EBOOK →This volume is a collection of research works to honor the late Professor Mark H.A. Davis, whose pioneering work in the areas of Stochastic Processes, Filtering, and Stochastic Optimization spans more than five decades. Invited authors include his dissertation advisor, past collaborators, colleagues, mentees, and graduate students of Professor Davis, as well as scholars who have worked in the above areas. Their contributions may expand upon topics in piecewise deterministic processes, pathwise stochastic calculus, martingale methods in stochastic optimization, filtering, mean-field games, time-inconsistency, as well as impulse, singular, risk-sensitive and robust stochastic control.
Author: Debasish Roy
Publisher: CRC Press
Published: 2024-01-25
Total Pages: 525
ISBN-13: 1000914445
DOWNLOAD EBOOK →This comprehensive textbook covers both classical and geometric aspects of optimization using methods, deterministic and stochastic, in a single volume and in a language accessible to non-mathematicians. It will help serve as an ideal study material for senior undergraduate and graduate students in the fields of civil, mechanical, aerospace, electrical, electronics, and communication engineering. The book includes: Derivative-based Methods of Optimization. Direct Search Methods of Optimization. Basics of Riemannian Differential Geometry. Geometric Methods of Optimization using Riemannian Langevin Dynamics. Stochastic Analysis on Manifolds and Geometric Optimization Methods. This textbook comprehensively treats both classical and geometric optimization methods, including deterministic and stochastic (Monte Carlo) schemes. It offers an extensive coverage of important topics including derivative-based methods, penalty function methods, method of gradient projection, evolutionary methods, geometric search using Riemannian Langevin dynamics and stochastic dynamics on manifolds. The textbook is accompanied by online resources including MATLAB codes which are uploaded on our website. The textbook is primarily written for senior undergraduate and graduate students in all applied science and engineering disciplines and can be used as a main or supplementary text for courses on classical and geometric optimization.
Author: George Yin
Publisher: Springer
Published: 2019-07-16
Total Pages: 599
ISBN-13: 3030254984
DOWNLOAD EBOOK →This volume collects papers, based on invited talks given at the IMA workshop in Modeling, Stochastic Control, Optimization, and Related Applications, held at the Institute for Mathematics and Its Applications, University of Minnesota, during May and June, 2018. There were four week-long workshops during the conference. They are (1) stochastic control, computation methods, and applications, (2) queueing theory and networked systems, (3) ecological and biological applications, and (4) finance and economics applications. For broader impacts, researchers from different fields covering both theoretically oriented and application intensive areas were invited to participate in the conference. It brought together researchers from multi-disciplinary communities in applied mathematics, applied probability, engineering, biology, ecology, and networked science, to review, and substantially update most recent progress. As an archive, this volume presents some of the highlights of the workshops, and collect papers covering a broad range of topics.
Author: Ioannis Dritsas
Publisher: BoD – Books on Demand
Published: 2011-02-28
Total Pages: 492
ISBN-13: 9533078294
DOWNLOAD EBOOK →Stochastic Optimization Algorithms have become essential tools in solving a wide range of difficult and critical optimization problems. Such methods are able to find the optimum solution of a problem with uncertain elements or to algorithmically incorporate uncertainty to solve a deterministic problem. They even succeed in fighting uncertainty with uncertainty. This book discusses theoretical aspects of many such algorithms and covers their application in various scientific fields.
Author: Alan Bain
Publisher: Springer Science & Business Media
Published: 2008-10-08
Total Pages: 395
ISBN-13: 0387768963
DOWNLOAD EBOOK →This book provides a rigorous mathematical treatment of the non-linear stochastic filtering problem using modern methods. Particular emphasis is placed on the theoretical analysis of numerical methods for the solution of the filtering problem via particle methods. The book should provide sufficient background to enable study of the recent literature. While no prior knowledge of stochastic filtering is required, readers are assumed to be familiar with measure theory, probability theory and the basics of stochastic processes. Most of the technical results that are required are stated and proved in the appendices. Exercises and solutions are included.
Author: Lifeng Ma
Publisher: CRC Press
Published: 2018-12-07
Total Pages: 180
ISBN-13: 0429761929
DOWNLOAD EBOOK →In this book, control and filtering problems for several classes of stochastic networked systems are discussed. In each chapter, the stability, robustness, reliability, consensus performance, and/or disturbance attenuation levels are investigated within a unified theoretical framework. The aim is to derive the sufficient conditions such that the resulting systems achieve the prescribed design requirements despite all the network-induced phenomena. Further, novel notions such as randomly occurring sensor failures and consensus in probability are discussed. Finally, the theories/techniques developed are applied to emerging research areas. Key Features Unifies existing and emerging concepts concerning stochastic control/filtering and distributed control/filtering with an emphasis on a variety of network-induced complexities Includes concepts like randomly occurring sensor failures and consensus in probability (with respect to time-varying stochastic multi-agent systems) Exploits the recursive linear matrix inequality approach, completing the square method, Hamilton-Jacobi inequality approach, and parameter-dependent matrix inequality approach to handle the emerging mathematical/computational challenges Captures recent advances of theories, techniques, and applications of stochastic control as well as filtering from an engineering-oriented perspective Gives simulation examples in each chapter to reflect the engineering practice
Author: Masanao Aoki
Publisher: Elsevier
Published: 2016-06-03
Total Pages: 372
ISBN-13: 1483224058
DOWNLOAD EBOOK →Optimization of Stochastic Systems
Author: Ranjan Ganguli
Publisher: CRC Press
Published: 2023-04-17
Total Pages: 252
ISBN-13: 1000829294
DOWNLOAD EBOOK →The digital twin of a physical system is an adaptive computer analog which exists in the cloud and adapts to changes in the physical system dynamically. This book introduces the computing, mathematical, and engineering background to understand and develop the concept of the digital twin. It provides background in modeling/simulation, computing technology, sensor/actuators, and so forth, needed to develop the next generation of digital twins. Concepts on cloud computing, big data, IoT, wireless communications, high-performance computing, and blockchain are also discussed. Features: Provides background material needed to understand digital twin technology Presents computational facet of digital twin Includes physics-based and surrogate model representations Addresses the problem of uncertainty in measurements and modeling Discusses practical case studies of implementation of digital twins, addressing additive manufacturing, server farms, predictive maintenance, and smart cities This book is aimed at graduate students and researchers in Electrical, Mechanical, Computer, and Production Engineering.
