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Inverse Problems for Stochastic Partial Differential Equations
English | 2026 | ISBN: 9789819590490 | 147 Pages | PDF EPUB (True) | 10 MB
This book provides a comprehensive and systematic introduction to inverse problems for stochastic partial differential equations (SPDEs), with particular emphasis on stochastic parabolic and hyperbolic equations. It addresses both the unique challenges and new opportunities that arise in the stochastic setting. Key topics include inverse state problems (such as determining unknown initial conditions) and inverse source problems (identifying unknown source terms), with a focus on the mathematical tools essential for their analysis, especially global Carleman estimates tailored to SPDEs. The book explores fundamental issues of uniqueness, stability, and reconstruction under various measurement scenarios, including internal, boundary, and terminal observations. It highlights how stochasticity can fundamentally alter the nature of inverse problems, sometimes enabling solutions where deterministic approaches fail. Reconstruction methods such as Tikhonov regularization are also discussed in detail. This book is intended for graduate students and researchers in applied mathematics, stochastic analysis, and PDEs, as well as practitioners in fields like mathematical finance, physics, and engineering who require rigorous methods for uncertainty quantification. A moderate background in PDEs, functional analysis, and basic stochastic calculus is beneficial.

Introductory Physical Geology
English | 2026 | ISBN: 3032202302 | 389 Pages | PDF (True) | 350 MB
Introductory Physical Geology, written by an author who has been the instructor of record for this course for approximately two decades and taught it via Face-To-Face, ONLINE, and Hybrid forms of content delivery, is designed to suit the needs of both students and instructors in college and high school. Well-illustrated diagrams and photographs are used extensively to simplify and illustrate concepts. The internet is also invoked to help students easily grasp the content and complement in-class activities. An extensive Test Bank and several ONLINE activities for assignments or homework, enable instructors to evaluate student learning objectives. Having taught this course at 8 different Universities in the United States as the Instructor of Record for almost 2 decades, the author's extensive experience gained during this time is evident in the manner the text and various forms of illustrations are utilized to aid the student learning experience. Additionally, the author brings together the boatload of knowledge, content delivery techniques, and materials that he acquired from having been taught by various instructors of different backgrounds, ethnicities, and expertise at the 5 universities in three different countries where he studied at and graduated from. Adopt this Textbook today!

Introductory Course on Differential Equations
English | 2014 | ISBN: 8184873999 | 598 Pages | PDF (True) | 4 MB
Introductory Course on DIFFERENTIAL EQUATIONS has been written as a textbook for a first course

Introduction to the Mathematical Physics of Nonlinear Waves
English | 2021 | ISBN: 0750337575 | 167 Pages | EPUB (True) | 8 MB
Written for students at upper-undergraduate and graduate levels, it is suitable for advanced physics courses on nonlinear physics. The book covers the fundamental properties of nonlinear waves, dealing with both theory and experiment. The aim is to emphasize established tools and introduce new methods underpinning important new developments in this field, especially as applied to solid-state materials. The updated edition has been extended to emphasize the importance of thermodynamics in a description of modulated crystals and contains new chapters on superconductivity that can be interpreted by the soliton mechanism. It is also updated to include new end-of-chapter problems.

Introduction to Tensor Network Methods: From Many-Body Quantum Systems to Machine Learning
English | 2026 | ISBN: 3032176344 | 339 Pages | PDF EPUB (True) | 45 MB
This second edition of the textbook Introduction to Tensor Network Methods contains more advanced and technical parts as new topics related to tensor network algorithms that have been developed in the last few years. The reader finds new chapters dedicated to tree tensor networks for high-dimensional systems as applications to lattice gauge theory. The implementation of tensor networks for machine learning is also presented in detail.

Introduction to Scientific Computation: A First Course for Physics, Mathematics and Engineering Majors
English | 2026 | ISBN: 9819815487 | 323 Pages | EPUB (True) | 3 MB
This book introduces scientific computation in an accessible and interactive way for undergraduates, especially those in the sciences, engineering, and mathematics. No prior programming experience is expected. Readers can begin directly from the introductory chapters, which cover basic elements of the Python programming language, then progress to the main chapters where techniques of scientific computation are presented and applied to specific problems. Examples and exercises are included in detail, covering diverse topics from various branches of science. These include cellular automata, the driven damped pendulum, Euler-Bernoulli beam theory, Newton's law of cooling, and many others.

Introduction to Numerical Mathematics: Theory, Practice and Numerous Examples
English | 2026 | ISBN: 3662725452 | 539 Pages | PDF EPUB (True) | 75 MB
This textbook presents the theoretical foundations of numerical mathematics in a modern, application-oriented, and comprehensive way. In addition to the standard content, it includes numerous examples and practical excursions to sustainably enhance understanding. Proofs are presented in a very detailed, step-by-step manner. The recurring core concepts of numerical mathematics like accuracy, efficiency, robustness and stability are explicitly addressed and clearly distinguished from one another. Specific example calculations for the described algorithms are carried out using provided Python codes. Numerical modeling for aspects of machine learning and neural networks are taken into consideration. Furthermore, numerical methods from linear algebra and analysis are presented separately, which significantly facilitates students' access to numerical mathematics - based on the two main lectures within undergraduate studies in mathematics. Therefore, the book is ideally suited for students of mathematics, physics, computer science, or engineering.

Introduction to Nonlinear Optics of Photonic Crystals and Metamaterials, Second Edition
English | 2021 | ISBN: 0750335777 | 229 Pages | PDF EPUB (True) | 14 MB
Introduction to Nonlinear Optics of Photonic Crystals and Metamaterials, Second Edition presents concise examples illustrating some of the basic principles of nanophotonics, embedding a discussion of current and future device applications in a theoretical account of the non-linear behaviour of light at nanoscale. It is an excellent access point for the current literature on nanophotonics, ideal for researchers, undergraduate and graduate students.

Introduction to Machine Learning: From Math to Code
English | 2025 | ISBN: 1316519503 | 678 Pages | PDF | 22 MB
Emphasizing how and why machine learning algorithms work, this introductory textbook bridges the gap between the theoretical foundations of machine learning and its practical algorithmic and code-level implementation. Over 85 thorough worked examples, in both Matlab and Python, demonstrate how algorithms are implemented and applied whilst illustrating the end result. Over 75 end-of-chapter problems empower students to develop their own code to implement these algorithms, equipping them with hands-on experience. Matlab coding examples demonstrate how a mathematical idea is converted from equations to code, and provide a jumping off point for students, supported by in-depth coverage of essential mathematics including multivariable calculus, linear algebra, probability and statistics, numerical methods, and optimization. Accompanied online by instructor lecture slides, downloadable Python code and additional appendices, this is an excellent introduction to machine learning for senior undergraduate and graduate students in Engineering and Computer Science.

Introduction to Electric Circuits, Devices, and Machines: Select Topics for Mechanical Engineers
English | 2026 | ASIN: B0GF4DPKJH | 334 Pages | PDF | 84 MB
Introduction to Electric Circuits, Devices, and Machines: Select Topics for Mechanical Engineers provides students with a practical guide to understanding and manipulating electrical signals and power. The book examines a variety of electrical topics that provide mechanical engineers with the knowledge and skills they need to succeed within their future careers. Structured into four parts, the book begins by introducing fundamental skills that support the learner as they develop applicable engineering skills. The subsequent parts introduce a variety of topics that engineers of all disciplines will find applicable in a modern engineering career. Topics include time-dependent circuit analysis, signal and measurement electronics, and electromagnetic devices and machines. Students learn to analyze the energy transfer between the electrical and mechanical subsystems of a design, collect and condition electric measurement signals, and drive electric motors. Introduction to Electric Circuits, Devices, and Machines has been tailored specifically for mechanical engineers and technologists who are interested in applying electrical concepts to mechanical systems.