Calin O Deep Learning Methods Of Mathematical Physics Vol I 2026 | 25.02 MB

Title: Deep Learning Methods of Mathematical Physics - Volume I: Direct and Inverse Problems (552 Pages)
Author: Ovidiu Calin

Description:
This textbook provides a thorough overview of mathematical physics, highlighting classical topics as well as recent developments. Readers will be introduced to a variety of methods that reflect current trends in research, including the Bergman kernel approach for solving boundary value and spectral problems for PDEs with variable coefficients. With its careful treatment of the fundamentals as well as coverage of topics not often encountered in textbooks, this will be an ideal text for both introductory and more specialized courses.
The first five chapters present standard material, including the classification of PDEs, an introduction to boundary value and initial value problems, and an introduction to the Fourier method of separation of variables. More advanced material and specialized treatments follow, including practical methods for solving direct and inverse Sturm-Liouville problems; the theory of parabolic equations, harmonic functions, potential theory, integral equations and the method of non-orthogonal series.
Methods of Mathematical Physics is ideal for undergraduate students and can serve as a textbook for a regular course in equations of mathematical physics as well as for more advanced courses on selected topics.



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Mratinkovic A Inorganic Chemistry 2ed 2023 | 11.26 MB

Title: Inorganic Chemistry
Author: Little Green Apples Publishing, LLC ™

Description:
The Inorganic Chemistry Study Brief is intended as a quick reference for students of Chemistry and lifelong learners interested in advanced scientific topics. It offers a concise overview of Inorganic Chemistry, including a brief history, definitions of key terms, notable chemists and their discoveries and a reduced-size Periodic Table of Elements chart. This Study Brief contains informative sections on matter, units of measurement, chemical reactions, physical processes, chemical equilibrium and chemical kinetics; as well as atomic structure, quantum number and atomic orbital. Key concepts of chemical nomenclature and stoichiometry, chemical bonding and properties of solutions are included; along with sections on acids, bases, gases, liquids and solids. Useful terms, concepts and key equations on molecular geometry, thermodynamics, quantum mechanics, electrochemistry and nuclear chemistry are included.



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Hochlaf M Handbook of Electronic Structure Theory Methods Applications 2026 | 39.08 MB

Title: Handbook of Electronic Structure Theory: Methods and Applications
Author: Majdi Hochlaf

Description:
Handbook of Electronic Structure Theory: Methods and Applications provides a much-needed learning resource that collects and demonstrates the various key methods involved in electronic structure theory, the feasibility and reliability of electronic structure calculations, and their applications using computational chemistry. With a particular focus on the most modern and recent problems that are typically poorly covered in existing, largely outdated book literature, this handbook is designed with early career researchers in mind. It is written primarily for masters, PhD, and postdoctoral students in theoretical and computational chemistry as well as experimental researchers wishing to apply quantum chemical methods in a critical way. Elements like summary boxes, worked examples, and downloadable datasets make this a holistic guide to the topic for learners from different backgrounds who require a deeper understanding of electronic structure theory. Sections focus on critical core theories, the most important recent developments, and future directions, including key topics such as the electronic excited states and the harnessing of machine learning. Finally, the book collects a range of key case study examples of applications, such as in biomolecules, in spectroscopy, and for use in catalysis, amongst others. - Provides comprehensive coverage of electronic structure theory and its application using computational chemistry - Written with consistent structure and pedagogical elements to maximize learning and understanding - Focuses on modern and the most recent problems and challenges in electronic structure theory (which have been poorly covered in existing books and literature)



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Sher F Artificial Intelligence in Chemical Engineering 2026 | 25.35 MB

Title: Artificial Intelligence in Chemical Engineering
Author: Farooq Sher

Description:
Artificial Intelligence in Chemical Engineering explores the integration of artificial intelligence (AI) into various facets of chemical engineering. The book introduces historical information, highlights current state and trends in AI applications, and discusses challenges and opportunities within the field. Foundational principles of AI and machine learning are thoroughly covered, giving readers a solid understanding of basic AI principles, machine learning algorithms, and the crucial processes of model training and validation. The book then delves into the critical phase of data acquisition and preprocessing for AI models, addressing strategies for data collection, ensuring data quality, and techniques for feature engineering and selection.Subsequent chapters cover a wide spectrum of AI applications in chemical engineering. From supervised and unsupervised learning for process modeling to the advanced realm of deep learning applications, this book explores neural networks, convolutional and recurrent architectures, and their real-world applications in process optimization and analysis. - Navigates the dynamic intersection of AI and chemical engineering, covering ethical considerations, interdisciplinary applications, and AI's impact on safety, sustainability, and innovation - Bridges the gap between policy and implementation of AI in chemical engineering, facilitating a harmonious integration of AI technologies and fostering responsible and effective use within the chemical engineering industry - Offers a forward-looking approach to guide professionals, researchers, and students in navigating the dynamic and transformative future of AI in chemical engineering



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Edwards M An Introduction to Quantum Computing for Computer Engineers 2026 | 4.68 MB

Title: An Introduction to Quantum Computing for Computer Engineers
Author: Marcus S. Edwards

Description:
This book focuses on practical topics in quantum computing that will give a student or practitioner of computer engineering a foundational understanding of quantum computer engineering. The reader should know enough by the end of the book to work effectively with quantum computers in a lab or industry setting, at any layer of the stack.
• Provides readers with a working knowledge of quantum mechanics without the need to read comprehensive textbooks;
• Includes prerequisite knowledge needed to begin working on any part of the quantum computing stack;
• Teaches useful skills throughout the book, such as nanofabrication, programming, PCB design and RF circuit design.



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Wufka M , Canonico M Introduction to Cloud Computing 2026 | 18.08 MB

Title: Introduction to CloudComputing
Author: Wufka, Michael/ Canonico, Massimo

Description:
This book aims to serve as an introductory textbook on cloud computing. The target audience are readers with some technical background or interest who are also interested in the business aspects of cloud computing. Examples are usually based on the Google Cloud Platform, but the principles covered in the book are equally relevant to users of other cloud platforms.
Even though cloud computing in its modern form arguably only started in 2006, when Amazon began to offer public cloud computing resources, it has almost become a default choice for organizations to operate their IT resources. Its wide adoption means that most IT professionals will work with cloud computing in various roles throughout their careers, making a basic understanding of the technology and its effective use an important part of their education.
Rather than focusing on a specific area of cloud computing, this book provides a broad overview of the topic, ranging from technical foundations to cloud architecture, cloud security, multi-cloud systems and the cloud-to-edge continuum. Various features such as review questions and hands-on exercise help readers understand the material covered in depth.



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Peña M Azure Data Fundamentals A Guide to DP-900 Certification and Beyond 2026 | 6.29 MB

Title: Azure Data Fundamentals
Author: Michael John Peña

Description:
Learn the essential skills and concepts for working with data in the cloud using Microsoft Azure. With this practical guide, professionals new to data management and Azure will learn how to leverage Azure services such as Azure Cosmos DB, Azure Storage, Azure SQL, and Microsoft Fabric to create, store, process, analyze, and visualize data.
Author Michael John Peña, principal data and application engineer, also shows you how to apply security, monitoring, and optimization techniques to your data solutions in Azure. Ideal as a resource for the Azure Data Fundamentals certification, this book also provides knowledge you can apply in your daily work even after platforms evolve and new technologies emerge.
You'll gain insights that will help you apply the fundamentals of data operations and engineering in the Azure ecosystem.

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• Learn core data concepts and the corresponding Microsoft Azure data services
  
• Discover how to work with relational and nonrelational data on Azure
  
• Build data analytics solutions using various Azure data services
  
• Identify and describe core data workloads and data analytics workloads on Azure
  
• Articulate how to work with data security and privacy on Azure
  
• Explain the use cases, benefits, and limitations of different Azure data services
  

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Pawitan Y In All Likelihood Statistical Modelling and Inference 2ed 2026 | 22.93 MB

Title: In All Likelihood
Author: Yudi Pawitan;

Description:
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Dovbush P , Krantz S A Second Course in Complex Analysis 2026 | 6.93 MB

Title: A Second Course in Complex Analysis
Author: Peter V. Dovbush;Steven G. Krantz;

Description:
Few other books purport to be a second course in complex analysis. This book differs in that it covers more modern topics and is more geometric in focus. Most texts on complex variable theory contain the same material. However, complex analysis is a vast and diverse subject with a long history and many aspects. A second course will benefit students and introduce these new topics that they might not otherwise experience.
Lars Ahlfors alone invented many new parts of the subject; Lipman Bers made decisive contributions, and there are many others. It is easy to justify a "second course" in complex analysis. That is what this book purports to be.
Some of the topics presented here are:

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• harmonic measure
  
• extremal length
  
• Riemann surfaces
  
• uniformization
  
• automorphism groups
  
• the Schwarz lemma and its generalizations
  
• analytic capacity
  
• the Bergman theory
  
• invariant metrics
  
• Picard's theorem
  
• the boundary Schwarz lemma
  

The goal is to expose the reader to unfamiliar parts of the subject of complex variables and perhaps to pique interest in further reading. As with the authors' other books, not only theorems and proofs are included, but also many examples and some exercises. Numerous graphics illustrate the key ideas.



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Cook D Interactively Exploring High-Dimensional Data and Models in R 2026 | 38.46 MB

Title: Interactively Exploring High-Dimensional Data and Models in R
Author: Dianne Cook;Ursula Laa;

Description:
Most data arrive with more than two numeric variables which means that plotting it on a computer screen or printed page presents a challenge: how do you visually explore for associations between more than two variables? Visualising data provides the opportunity to discover what we never expected, because it requires fewer assumptions to be made. Visualising elements of a model fit is a primary way to diagnose whether the fit matches this data. Two of more numeric variables is considered to be multivariate data, and when there are substantially more we would consider it to be high-dimensional data. This book provides you with the tools to visually explore high dimensions, to uncover associations, clustering and anomalies that may be missed when only using common methods for plotting one or two variables. It also illustrates how to use visualisation to understand how your model is operating on the data, to be able to explain how it is arriving at decisions. To make effective use of this material the reader should have a basic working knowledge of R and some understanding of multivariate statistical methods or machine learning methods. The book could form an independent course on visualization or be used as part of courses on multivariate statistical methods or machine learning.
High-dimensional data visualisation is valuable for understanding dimension reduction methods, unsupervised and supervised classification. This book is organised into these three topics, following overview and introductory chapters. The dimension reduction chapters cover principal component analysis and nonlinear dimension reduction. The chapters on cluster analysis cover hierarchical and k-means algorithms, model-based and self-organising maps, and finish with ways to communicate results and how to compare different results. The chapters on classification cover linear discriminant analysis, tree and forest algorithms, support vector machines and neural networks. We explain how to break down a neural network to examine the components, how to visualize predictive probabilities, and how to incorporate explainable AI metrics to develop a deeper understanding about how the model operates.



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