Uncertainty and risk are integral to engineering because real systems have inherent ambiguities that arise naturally or due to our inability to model complex physics. The authors discuss probability theory, stochastic processes, estimation, and stochastic control strategies and show how probability can be used to model uncertainty in control and estimation problems. The material is practical and rich in research opportunities.The authors provide a comprehensive treatment of stochastic systems from the foundations of probability to stochastic optimal control. The book covers discrete- and continuous-time stochastic dynamic systems leading to the derivation of the Kalman filter, its properties, and its relation to the frequency domain Wiener filter as well as the dynamic programming derivation of the linear quadratic Gaussian (LQG) and the linear exponential Gaussian (LEG) controllers and their relation to H2 and H-inf controllers and system robustness.Stochastic Processes, Estimation, and Control is divided into three related sections. First, the authors present the concepts of probability theory, random variables, and stochastic processes, which lead to the topics of expectation, conditional expectation, and discrete-time estimation and the Kalman filter. After establishing this foundation, stochastic calculus and continuous-time estimation are introduced. Finally, dynamic programming for both discrete-time and continuous-time systems leads to the solution of optimal stochastic control problems, resulting in controllers with significant practical application.

Stochastic Processes, Estimation, and Control (Advances in Design and Control)

Science & Math

Jason L. Speyer

Walter H. Chung

Mathematics

Society for Industrial & Applied Mathematics,U.S.

Optimization is of critical importance in engineering. Engineers constantly strive for the best possible solutions, the most economical use of limited resources, and the greatest efficiency. As system complexity increases, these goals mandate the use of state-of-the-art optimization techniques.
 In recent years, the theory and methodology of optimization have seen revolutionary improvements. Moreover, the exponential growth in computational power, along with the availability of multicore computing with virtually unlimited memory and storage capacity, has fundamentally changed what engineers can do to optimize their designs. This is a two-way process: engineers benefit from developments in optimization methodology, and challenging new classes of optimization problems arise from novel engineering applications.
 Advances and Trends in Optimization with Engineering Applications reviews 10 major areas of optimization and related engineering applications, providing a broad summary of state-of-the-art optimization techniques most important to engineering practice. Each part provides a clear overview of a specific area and discusses a range of real-world problems.
 The book provides a solid foundation for engineers and mathematical optimizers alike who want to understand the importance of optimization methods to engineering and the capabilities of these methods.
 Audience: This book will be of interest to doctoral students, recent graduates, experienced researchers, and practitioners in engineering, optimization, and operations research.

Advances and Trends in Optimization with Engineering Applications (MOS-SIAM Series on Optimization)

Tamas Terlaky

Miguel F. Anjos

Shabbir Ahmed

Linear Stochastic Systems, originally published in 1988, is today as comprehensive a reference to the theory of linear discrete-time-parameter systems as ever. Its most outstanding feature is the unified presentation, including both input-output and state space representations of stochastic linear systems, together with their interrelationships. The author first covers the foundations of linear stochastic systems and then continues through to more sophisticated topics including: the fundamentals of stochastic processes and the construction of stochastic systems; an integrated exposition of the theories of prediction, realization (modeling), parameter estimation, and control; and a presentation of stochastic adaptive control theory. Written in a clear, concise manner and accessible to graduate students, researchers, and teachers, this classic volume also includes background material to make it self-contained and has complete proofs for all the principal results of the book. Furthermore, this edition includes many corrections of errata collected over the years.

Linear Stochastic Systems (Classics in Applied Mathematics)

Caines, Peter E.

Jobs using mathematics, statistics, and operations research are projected to grow by almost 30% over the next decade. BIG Jobs Guide helps job seekers at every stage of their careers in these fields explore opportunities in business, industry, and government (BIG). Written in a conversational and practical tone, BIG Jobs Guide offers insight on topics such as: What skills can I offer employers? How do I write a high-impact resume? Where can I find a rewarding internship? What kinds of jobs are out there for me? The Guide also offers insights to advisors and mentors on topics such as how departments can help students get BIG jobs and how faculty members and internship mentors can build institutional relationships. Whether you're an undergraduate or graduate student or a job seeker in applied mathematics, statistics, computer science, or operations research, this hands-on book will help you reach your goal, whether landing an internship, getting your first job or transitioning to a new one.

BIG Jobs Guide: Business, Industry, and Government Careers for Mathematical Scientists, Statisticians, and Operations Researchers (Other Titles in Applied Mathematics)

Rachel Levy

Richard Laugesen

Fadil Santosa

Bits and Bugs A Scientific and Historical Review of Software Failures in Computational Science Software, Environments, and Tools 29 In scientific computing (also known as computational science), advanced computing capabilities are used to solve complex problems. This self-contained book describes and analyzes reported software failures related to the major topics within scientific computing: mathematical modeling of phenomena; numerical analysis (number representation, rounding, conditioning); mathematical aspects and complexity of algorithms, systems, or software; concurrent computing (parallelization, scheduling, synchronization); and numerical data (such as input of data and design of control logic). Readers will find lists of related, interesting bugs, MATLAB examples, and excursions that provide necessary background, as well as an in-depth analysis of various aspects of the selected bugs. Illustrative examples of numerical principles such as machine numbers, rounding errors, condition numbers, and complexity are also included. This book is intended for students, teachers, and researchers in scientific computing, computer science, and applied mathematics. It is also an entertaining and motivating introduction for those with a minimum background in mathematics or computer science. Bits and Bugs can be used for courses in numerical analysis, numerical methods in linear algebra/ODEs/PDEs, introductory software engineering, introductory scientific computing, and parallel programming.

Bits and Bugs: A Scientific and Historical Review of Software Failures in Computational Science

Thomas Huckle

Tobias Neckel

Building on the authors’ many years of experience running creativity workshops, How to Be Creative: A Practical Guide for the Mathematical Sciences • gives a six-step process for generating great ideas that can be used by individuals or groups, • provides examples demonstrating how these concepts have been or might be used in practice in the mathematical sciences, and • presents seven cases of tried and tested briefs that can be used at creativity workshops. With mathematically oriented models, this book is for anyone in the mathematical sciences who wants to be more creative or who wishes to train others in creativity.

How to Be Creative: A Practical Guide for the Mathematical Sciences

Nicholas J. Higham

Dennis Sherwood

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Books by Society for Industrial & Applied Mathematics,U.S.