Publisher: Springer Nature Switzerland / Springer
Publication Date: Expected July 21, 2026
Series: Springer Optimization and Its Applications
Format: Hardcover (approx. 590 pages)
Language: English
ISBN-13: 978-3-032-21692-2 (9783032216922)
ISBN-10: 3032216923
Subject Categories: Mathematical Analysis, Functional Analysis, Optimization, Number Theory, Interdisciplinary Mathematics
This volume provides a comprehensive overview of recent theoretical breakthroughs and real-world applications within contemporary mathematical analysis. It serves as a structural platform highlighting the cross-pollination between abstract mathematical theories and interdisciplinary problem-solving across fields like physics, data processing, and economics.
The text features an array of expert contributions that discuss theory and problems in real and complex analysis, operator theory, analytic inequalities, and nonlinear systems. Rather than focusing solely on isolated sub-disciplines, the volume is intentionally broad, connecting concepts from analytic number theory (such as spectral properties of arithmetic functions and Riemann zeta zeros) to topological structures like relator spaces, and stochastic models (such as non-reversible Markov chains and random walks in random environments). It is designed to guide graduate students and active researchers seeking an updated toolkit for high-dimensional mathematical optimization and continuous analysis.
While the complete detailed index of all compiled papers is finalizing layout for its summer 2026 rollout, the structural sequence begins with the following cornerstone core chapters:
Chapter 1: Elementary Arithmetic Functions and Their Connection to the Non-Trivial Zeta Zeros
Chapter 2: Residues, Borders and Boundaries in Relator Spaces
Chapter 3: Study of Cycle Chains Associated with Non-Reversible Markov Chains Describing a Random Walk with Jumps in Random Environments
Chapter 4: Analytic Inequalities and Operator Variations in Multi-Dimensional Spaces
Chapter 5: Advancements in Geometric Functional Analysis and Functional Equations
Chapter 6: Theoretical Frameworks for Nonlinear Optimization and Approximation Systems
Chapter 7: Interdisciplinary Intersections: Applications in Mathematical Physics and Computational Economics
Publisher: Springer Verlag GmbH / Springer Nature Switzerland
Publication Date: Expected July 19, 2026
Series: Springer Proceedings in Mathematics & Statistics (Volume 550)
Format: Hardcover
Language: English
ISBN-13: 978-3-032-22351-7 (9783032223517)
Subject Categories: Applied Mathematics, Computational Modeling, Differential Equations, Artificial Intelligence, Numerical Analysis
This proceedings volume compiles a curated selection of peer-reviewed papers presented at the First International Itinerant Congress on Applied Mathematics (IICAM 2025). Hosted by the Faculty of Sciences and Techniques in Errachidia, Moulay Ismail University, Morocco, the hybrid event drew mathematics and computer science researchers from across North Africa, Europe, and the Middle East.
Conceived as a traveling ("itinerant") conference that rotates through different academic institutions, this inaugural volume balances rigorous mathematical theory with interdisciplinary breakthroughs. The core content focuses heavily on:
Differential Equations & Numerical Methods: Elliptic and fractional partial differential equations (PDEs), variational methods, and optimal control theory.
Computational Modeling & Computer Science: Advanced mathematical programming alongside data-driven machine learning models and applications in artificial intelligence.
The volume offers an essential toolkit for postgraduate students, applied mathematicians, and computer scientists researching predictive modeling and optimization algorithms.
As an extensive inaugural conference proceedings collection, the chapters are partitioned into the following main thematic streams:
Part I: Elliptic and Fractional Partial Differential Equations (Analytical proofs, boundary conditions, and solution properties)
Part II: Variational Methods and Optimal Control Theory (Mathematical programming, system constraints, and functional optimization)
Part III: Numerical Analysis and Differential Modeling (Algorithmic stability, fluid and structural simulations)
Part IV: Mathematical Contributions to Computer Science & Artificial Intelligence (Data-driven learning methods, neural network mathematics, and predictive modeling)
Publisher: Springer Nature Switzerland / Springer Berlin
Publication Date: Expected July 21, 2026
Series: Springer Proceedings in Mathematics & Statistics
Format: Hardcover (approx. 200–250 pages)
Language: English
ISBN-13: 978-3-032-24140-5 (9783032241405)
Subject Categories: Statistics, Econometrics, Mathematical Modeling, Machine Learning, Longitudinal Analysis
This volume compiles the academic proceedings and peer-reviewed outputs generated by the collaborative research units of the PRIN 2022 project "Hidden Markov Models for Early Warning Systems (HMM-EWS)." The content captures innovations from three major workshops held across Italy between 2024 and 2026.
The monograph focuses on the development, optimization, and real-world deployment of advanced classes of models for longitudinal data, putting a heavy emphasis on Hidden Markov Models (HMMs) as robust tools for predicting rare and critical events. Across its chapters, the text tackles fundamental methodological hurdles such as massive data imbalances, structural uncertainty, and cost-sensitive classifications.
Rather than remaining entirely theoretical, the book bridges these advanced statistical methods into empirical evidence of strong policy relevance. Applications span multiple scientific domains, including financial econometrics (forecasting banking crises and cryptocurrency market volatility), network science (link predictions in temporal networks), and health economics (evaluating care demands). In line with open science principles, the research findings are accompanied by reproducible code frameworks written in Python, R, and STATA.
The book structures its multi-disciplinary findings across 8 core chapters:
Chapter 1: Presentation of the Proceedings Book of the Project PRIN
Chapter 2: Methodological Innovations in Hidden Markov Models for Longitudinal Frameworks
Chapter 3: Managing Data Imbalance and Cost-Sensitive Classification in Rare Event Forecasting
Chapter 4: Financial Econometrics Applications: Predicting Banking Crises and Structural Tail Risk
Chapter 5: Stochastic Modeling of Financial Markets and Cryptocurrency Volatility Shifts
Chapter 6: Network Science and Link Prediction Methodologies in Temporal Networks
Chapter 7: Health Economics and Longitudinal Analytics of Primary versus Secondary Care Demands
Chapter 8: Bayesian Systems and Machine Learning Integrations for Early Warning Systems (EWS)
Publisher: Birkhäuser / Springer Nature Switzerland AG
Publication Date: Expected September 2026
Series: Applied and Numerical Harmonic Analysis
Format: Hardcover
Language: English
ISBN-13: 978-3-032-23850-4 (9783032238504)
ISBN-10: 3032238501
Subject Categories: Harmonic Analysis, Functional Analysis, Spectral Theory, Signal Processing, Geometric Analysis
This volume is a mathematical tribute dedicated to the memory and legacy of the distinguished mathematician Jean-Pierre Gabardo (1958–2024), whose deep and highly original work significantly shaped classical harmonic analysis, spectral-tile duality, and non-commutative harmonic structures.
The collection brings together cutting-edge research chapters authored by his closest colleagues, co-authors, and friends. An opening introductory chapter reviews Gabardo’s wide-ranging mathematical career and outlines his lasting contributions as an academic mentor.
The core research content is structured around three major focal points:
Functional and Harmonic Analysis: Unveiling deeper operator behaviors and foundational abstractions.
Analysis of Frames and Non-Orthogonal Expansions: Investigating redundancies and signal constructions.
Sampling and Spectral Theory: Examining the geometric and algebraic intersections of data discretization, tiling configurations, and dualities.
The text serves as a definitive resource for advanced researchers and graduate students seeking insight into modern developments within computational Fourier analysis and geometric metric theories.
While the complete indexing of all individual papers is under final layout for its late 2026 release, the volume is partitioned into distinct thematic fields and features the following cornerstone sections:
Introductory Chapter: The Life, Legacy, and Mentorship of Jean-Pierre Gabardo
Part I: Functional and Harmonic Analysis (Developments in classical and non-commutative harmonic structures)
Part II: Analysis of Frames and Non-Orthogonal Expansions
Featuring: Quantum Gabor frames on Locally Compact Abelian (LCA) groups
Part III: Sampling, Spectral Theory, and Geometric Layouts
Featuring: Translational tiles of $\mathbb{R} \times \mathbb{Z}^2$ and wavelet sets in the form of two intervals
Featuring: Beurling densities and frames of exponentials on complex structures
Publisher: World Scientific Publishing Co.
Publication Date: Expected June/July 2026
Pages: Approx. 248 pages
Format: Hardcover
Language: English
ISBN-13: 978-1-80061-925-8 (9781800619258)
ISBN-10: 1800619258
Subject Categories: Representation Theory, Group Theory, Non-Commutative Harmonic Analysis, Mathematical Physics
This research monograph develops a unified analytical framework for understanding how von Neumann algebras, quasi-invariant measures, and probabilistic methods interact in the representation theory of non-locally-compact groups. In these high-dimensional spaces, classical tools such as the Haar measure are not available, requiring alternative mathematical foundations.
The text is divided into two comprehensive structural focal points:
Infinite-Dimensional Groups: The book investigates representations of infinite-dimensional groups, including the general linear group configurations like $GL_0(2^\infty, \mathbb{R})$ and infinite upper-triangular matrices. It utilizes operator-algebraic and measure-theoretic techniques to establish exact conditions for irreducibility (addressing the famous Ismagilov conjecture via regular Gaussian representations). By connecting ergodic theory with algebraic structures, it introduces new methods for constructing representations that capture complex symmetries.
Braid and Quantum Groups: The second portion explores the quantization and linearity of braid groups ($B_n$). It investigates the structural connections between representations of braid groups (such as the Lawrence–Krammer and Burau representations) and the highest weight modules of quantum groups like $U_q(\mathfrak{sl}_2)$.
The book partitions its technical chapters across two primary research parts:
Part I: Unitary Representations of Infinite-Dimensional Groups and the Ismagilov Conjecture
Introduction to Non-Locally-Compact Groups and Quasi-Invariant Measures
Von Neumann Algebras Generated by Regular and Quasi-Regular Representations
Criteria for Irreducibility and Equivalence of Regular Gaussian Representations
Induced Representations on Infinite-Dimensional Homogeneous Spaces
Part II: Representations of Braid Groups, Quantum Groups, and Geometric Linearity
Foundations of Braid Groups ($B_n$) and Quantum Deformations
The Burau Representation and Quantization of Symmetric Squares
The Lawrence–Krammer Representation and Linearity Properties
Highest Weight Modules of $U_q(\mathfrak{sl}_2)$ and Braid Group Symmetries
Publisher: World Scientific Publishing
Publication Date: Expected July 6, 2026
Format: Hardcover
Language: English
ISBN-13: 978-1-80061-907-4 (9781800619074)
Approximate Page Count: 200 pages
This volume serves as a modern bridge between traditional mathematical physics and contemporary artificial intelligence. As solving complex differential equations and tackling "inverse problems" (where one seeks to identify the parameters of a system from observed data) remains a computational bottleneck in many fields, this text introduces Physics-Informed Neural Networks (PINNs) and other machine learning frameworks as efficient alternatives to classical numerical methods.
Part I focuses on the fundamental concepts and practical methodologies, guiding the reader through the transition from symbolic solvers to neural approximation techniques. The authors—both prominent figures in applying data-driven approaches to hard science problems—include sets of problems and solutions, making this an ideal guide for researchers and students in applied mathematics, engineering, and the physical sciences who wish to leverage AI for complex scientific modeling.
While the specific chapter-by-chapter index is finalizing for its July 2026 release, the structure follows this core pedagogical progression:
I. Foundations: From classical numerical methods (Finite Difference, Finite Element) to neural network function approximation.
II. Physics-Informed Neural Networks (PINNs): Mathematical formulation and integrating physical laws (governing equations) into loss functions.
III. Inverse Problems: Techniques for parameter estimation and latent variable discovery in noisy data environments.
IV. Applied Case Studies: Selected problems from fluid dynamics, signal processing, and astrophysical modeling.
V. Exercises and Solutions: A dedicated section featuring guided practice problems to ensure mastery of implementation and theoretical understanding.
Publisher:
Publication Date: July 2026
Format: Hardcover
Language: English
ISBN-13: 978-981-98-2920-0
Subject: Mathematics / Competition Math
This book serves as a comprehensive record of the most challenging mathematical competition problems in China during the 2023–2024 academic year. It is primarily based on the test and selection questions from the 2024 Chinese National Training Team.
Key features include:
Problem Collection: Compiles problems and solutions from major domestic math competitions held between August 2023 and July 2024.
IMO Coverage: Includes problems from the 2024 International Mathematical Olympiad.
Expert Solutions: Solutions are provided by members of the Chinese National Training Team and national team coaches, showcasing ingenious and elegant problem-solving approaches.
While the detailed chapter-by-chapter layout is finalized with the publisher's release, the volume includes the following key sections:
Preface
The 21st Chinese Mathematical Olympiad Cooperation Summer Camp
The Hope League of Chinese Mathematical Olympiad Summer Camp (2023)
The 5th Northern Star of Hope Mathematical Summer Camp
The 4th China Centennial Schools Mathematics Competition
China National Team Selection Test (2024)
Publisher: World Scientific Publishing
Publication Date: Expected 2026
Language: English
ISBN-13: 978-981-98-2698-8 (9789819826988)
Subject Categories: Linear Algebra, Data Science, Artificial Intelligence, Python Programming, Applied Mathematics
This textbook provides a modern, integrated approach to linear algebra by blending foundational mathematical theory with practical implementation in Python. It is designed to serve students and professionals across mathematics, data science, engineering, and the physical sciences.
The book transitions from core linear algebraic concepts to their functional applications in Artificial Intelligence and machine learning algorithms. By leveraging Python, it allows readers to visualize concepts and solve complex systems numerically, making it an excellent resource for those who want to understand the "math behind the AI" through hands-on computation.
Key features include:
Core Fundamentals: Systematic coverage of systems of linear equations, matrix operations, and Row-Reduced Echelon Form (RREF).
Advanced Concepts: Clear explanations of eigenvalues, eigenvectors, inner product spaces, and Singular Value Decomposition (SVD).
Computational Integration: Python-based examples that demonstrate how to implement these mathematical structures in real-world data science tasks.
Based on the pedagogical structure of the work, the content is organized as follows:
Introduction to Linear Systems (Matrix operations and Row-Reduced Echelon Form)
Vector Spaces and Subspaces (Fundamentals and linear independence)
Linear Transformations and Matrices
Determinants and Their Applications
Eigenvalues and Eigenvectors (Structural analysis of linear operators)
Inner Product Spaces and Orthogonality
Singular Value Decomposition (SVD) and Dimensionality Reduction
Python for Linear Algebra (Practical coding implementation of the theoretical chapters)
Applications in Artificial Intelligence (Connecting matrix structures to neural networks and ML models)
Publisher: World Scientific Publishing
Publication Date: Expected 2026
Format: Hardcover (Gebunden)
Language: English
ISBN-13: 978-981-9829-23-1 (9789819829231)
Subject Categories: Mathematics, Measure Theory, Functional Analysis, Topology
This volume provides a rigorous, intermediate-level introduction to modern measure theory, with a central focus on Radon measures on general Hausdorff topological spaces. It is designed to bridge the gap between elementary introductory texts and highly specialized research monographs.
The book is particularly valuable because it clarifies the interplay between various definitions of Radon measures—such as inner-Radon, Riesz-Radon, Carathéodory-Radon, and Fremlin-Radon measures—which are often documented inconsistently in existing literature, especially in non-locally compact settings. By establishing clear equivalences and connections, the text provides a unified framework that is highly useful for researchers in functional analysis, probability, and mathematical economics.
Key highlights include:
Foundational Progression: Begins with measures on semirings and develops abstract foundations.
Advanced Frameworks: Covers Maharam measures, locally determined measures, and decomposable measures.
Core Theorems: Includes generalized Radon–Nikodym and Riesz–Markov type theorems, as well as treatments of product measures and invariant Radon measures under group actions.
Based on the book's pedagogical structure, it covers the following core areas:
Foundations of Measure Theory on Semirings
Abstract Measure-Theoretic Frameworks
Radon Measures on Hausdorff Spaces (Definitions and interconnections)
Non-Locally Compact Settings and Equivalences
Advanced Topics:
Maharam Measures
Locally Determined and Decomposable Measures
Product Measures and Fubini’s Theorem
Generalized Theorems:
Radon–Nikodym Theorems
Riesz–Markov–Kakutani Representations
Invariant Radon Measures under Group Actions