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Mathematics and Computer Science, Volume
4

Mathematics and Computer Science, Volume 4

Probability Theory And Stochastic Processes With Applications

Bring mathematical principles to bear on engineering problems with this updated text The evolution of industrial processes has resulted in greater emphasis upon analytical and numerical problem solving. Process improvement through experimentation is impractical and consequently engineers must rely upon computational and technical analysis. Furthermore, the ease with which time-series data can be collected and processed has made harmonic signal interpretation routine. Thus, the ability of engineers to analyze, model, compute, and interpret process phenomena is crucial to professional practice. Problem Solving in Engineering meets these needs with a foundational introduction to mathematical techniques in applied sciences and engineering. Incorporating examples from a range of scientific fields, it communicates principles that can be adapted to many hardware-software combinations. Now fully updated to reflect the latest research and applications, it remains an essential tool for engineers and applied scientists everywhere. Readers of the second edition will also find: <ul><li>Extensive time devoted to problem formulation</li><li>Detailed discussion of integro-differential equations and the processing and analysis of time-series data</li><li>The use of vorticity transport for the solution of momentum, heat, and mass transfer problems in two dimensions</li><li>Examples and problems drawn from aviation, telegraphy, structural failures, railroad operation, chemical processes, automatic process control, seismology, neutron diffusion, gravitation, and quantum theory</li><li>Many additional narrative-type exercises written to appeal to students who find problems in context better suited to their learning style</li><li>Solutions manual available for qualified instructors</li></ul> Problem Solving in Engineering is ideal for advanced undergraduate, graduate students, and technical professionals in the physical sciences, specifically chemical, civil, biochemical, electrical, and mechanical engineering, as well as physics, chemistry, and biology.

Analytical Mathematics and Numerical Analysis

Problem Solving in Engineering

Series in Materials Science and Engineering

This book provides an introduction to the geometrical and topological methods that have become essential tools for soft condensed matter physicists. The emphasis is on providing mathematical tools in clear but precise language, focusing on fundamental concepts using real examples to illustrate for readers in vivid, three-dimensional fashion. The motivation is clearly provided using problems from soft condensed matter, from the packing of colloids to liquid crystals and thermal fluctuations of lipid membranes.

Geometry and Topology of Soft Materials

This book covers a broad range of questions related to the importance of cell geometry in many areas, including nuclear positioning, chromosome dynamics, cytoskeletal remodeling, tissue patterning, cell-matrix and multicellular contacts. It also addresses medical implications, from its implication in the development of tumors to its role in fibrosis. Together, the chapters in this book will highlight current knowledge and modern approaches to investigating the biophysical design principles underlying key biological functions, with the overall goal of elucidating the connections between cell geometry and nuclear mechanics.

Cell Geometry

Smartmaths Standard 3

Smartmaths Infant 2

Number Systems: A Path into Rigorous Mathematics aims to introduce number systems to an undergraduate audience in a way that emphasises the importance of rigour, and with a focus on providing detailed but accessible explanations of theorems and their proofs.

A Path into Rigorous Mathematics

Number Systems

Advanced Engineering Mathematics, 11th Edition, is known for its comprehensive coverage, careful and correct mathematics, outstanding exercises, and self-contained subject matter parts for maximum flexibility. It opens with ordinary differential equations and ends with the topic of mathematical statistics. The analysis chapters address: Fourier analysis and partial differential equations, complex analysis, and numeric analysis. The book is written by a pioneer in the field of applied mathematics. This comprehensive volume is designed to equip students and professionals with the mathematical tools necessary to tackle complex engineering challenges and drive innovation. &nbsp; This edition of the text maintains those aspects of the previous editions that have led to the book being so successful. In addition to introducing a new appendix on emerging topics in applied mathematics, each chapter now features a dedicated section on how mathematical modeling and engineering can address environmental and societal challenges, promoting sustainability and ethical practices. This edition includes a revision of the problem sets, making them even more effective, useful, and up-to-date by adding the problems on open-source mathematical software.

Advanced Engineering Mathematics, International Adaptation

De Gruyter Studies in Mathematical Physics

Mirror symmetry is one of the most exciting ideas in mathematics to emerge in the last two decades. It was introduced in physics as a duality between superconformal field theories. This book discusses this rapidly developing aspect of string theory.

Laurent Polynomials in Mirror Symmetry

Math Survival Guide: Tips for Science Student, 3E

An essential guide for tackling outliers and anomalies in machine learning and data science. In recent years, machine learning (ML) has transformed virtually every area of research and technology, becoming one of the key tools for data scientists. Robust machine learning is a new approach to handling outliers in datasets, which is an often-overlooked aspect of data science. Ignoring outliers can lead to bad business decisions, wrong medical diagnoses, reaching the wrong conclusions or incorrectly assessing feature importance, just to name a few. Fundamentals of Robust Machine Learning offers a thorough but accessible overview of this subject by focusing on how to properly handle outliers and anomalies in datasets. There are two main approaches described in the book: using outlier-tolerant ML tools, or removing outliers before using conventional tools. Balancing theoretical foundations with practical Python code, it provides all the necessary skills to enhance the accuracy, stability and reliability of ML models. Fundamentals of Robust Machine Learning readers will also find: <ul><li>A blend of robust statistics and machine learning principles</li><li>Detailed discussion of a wide range of robust machine learning methodologies, from robust clustering, regression and classification, to neural networks and anomaly detection</li><li>Python code with immediate application to data science problems</li></ul> Fundamentals of Robust Machine Learning is ideal for undergraduate or graduate students in data science, machine learning, and related fields, as well as for professionals in the field looking to enhance their understanding of building models in the presence of outliers.

Mathematics and Computer Science, Volume 4

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