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AAP Advances in Materials, Manufacturing and Computational Intelligence Techniques

Artificial Intelligence in Mathematics
Rethinking Problems and Emerging Trends

Editors: Dr. Ashis Kumar Pati
Dr. Apul Narayan Dev
Dr. Swadhin Kumar Barisal
Dr. Mukesh Kumar Awasthi

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Artificial Intelligence in Mathematics

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Series: AAP Advances in Materials, Manufacturing and Computational Intelligence Techniques

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Short description about the volume:


The book, Artificial Intelligence in Mathematics: Rethinking Problems and Emerging Trends, will provide a comprehensive and forward-looking treatment of the transformative role of artificial intelligence (AI) in mathematical reasoning, discovery, modelling, and scientific computation. It will explore the rapidly evolving intersection of mathematics and AI, beginning with the foundations of formal mathematical reasoning, automated proof generation, neuro-symbolic architectures, and large language models for theorem proving. The volume will then progress toward AI-based learning of mathematical structures, multi-step reasoning, conjecture generation, automated discovery of mathematical identities, and intelligent approaches to algebra and number theory.

The book plants to further examine the application of AI and machine learning to mathematical modelling and scientific computing, covering high-dimensional numerical optimization, graph neural networks for combinatorial problems, topological data analysis, AI-driven differential equation solvers, symbolic regression, and adaptive mathematical modelling. These contributions demonstrate how data-driven and computational intelligence techniques can complement traditional mathematical methods and provide new approaches to complex problems that are difficult to address using conventional analytical and numerical techniques alone.

A distinctive feature of the volume will be its emphasis on human-centered and trustworthy mathematical intelligence. It will address human–AI collaboration in mathematical research, explainability and interpretability of AI-generated proofs, and issues related to bias, reliability, verification, and scientific credibility in AI-based mathematical systems. By bringing these perspectives together, the book highlights the importance of developing AI systems that are not only capable of generating mathematical results but also transparent, verifiable, reliable, and useful to mathematicians and researchers.

The final section will explore emerging frontiers in AI-enabled mathematics, including AI-assisted exploration of open mathematical problems, quantum machine learning, advanced mathematical computation, and new paradigms for mathematical discovery. These chapters will examine how AI may increasingly contribute to conjecture formation, hypothesis generation, symbolic and numerical experimentation, and the identification of previously unknown mathematical relationships, while also considering the changing role of human expertise in the mathematical discovery process.

This edited volume aims to bring together mathematicians, computer scientists, AI researchers, data scientists, computational scientists, educators, and interdisciplinary researchers to examine the current state and future directions of AI in mathematics. Rather than focusing exclusively on individual AI algorithms, the book develops an integrated perspective encompassing formal reasoning, machine learning, symbolic computation, mathematical modeling, scientific computing, explainable AI, human–AI collaboration, and emerging computational paradigms.

The volume will serve as a valuable reference for graduate students, researchers, faculty members, and professionals working in applied and pure mathematics, artificial intelligence, machine learning, computer science, scientific computing, mathematical modeling, and data science. It is particularly relevant for researchers interested in how AI can augment traditional mathematical thinking while preserving mathematical rigor, interpretability, and verifiability. By connecting foundational mathematical reasoning with emerging AI technologies, the book seeks to provide a coherent framework for understanding how artificial intelligence is reshaping mathematical research and how mathematics, in turn, can contribute to the development of more rigorous and trustworthy intelligent systems.

Coverage

Submission of book chapters should be focused on the following key highlights:

Part I: Foundations of AI in Mathematical Reasoning

Chapter 1: Formalizing Mathematical Reasoning in the Age of Artificial Intelligence

Chapter 2: Benchmarking AI Systems on Formal Proof Generation

Chapter 3: Neuro-Symbolic Architectures for Mathematical Problem Solving

Chapter 4: Large Language Models for Automated Theorem Proving: Capabilities and Limits

Part II: Learning Mathematical Structures and Intelligent Reasoning

Chapter 5: Learning Mathematical Structures from Data Using Deep Representation Learning

Chapter 6: Reinforcement Learning Strategies for Multi-Step Mathematical Reasoning

Chapter 7: Generative Models for Mathematical Conjecture Discovery

Chapter 8: Automated Discovery of Identities in Algebra and Number Theory
Part III: AI-Driven Mathematical Modeling and Scientific Computing

Chapter 9: AI-Augmented Numerical Optimization in High-Dimensional Spaces

Chapter 10: Graph Neural Networks for Combinatorial Optimization Problems

Chapter 11: Topological Data Analysis Enhanced by Machine Learning

Chapter 12: AI-Driven Approaches to Differential Equation Solving

Chapter 13: Symbolic Regression via AI for Scientific Discovery

Chapter 14: Adaptive Mathematical Modeling Using Machine Learning

Part IV: Human-Centered AI and Trustworthy Mathematical Intelligence

Chapter 15: Human-AI Co-Creation in Mathematical Research Workflows

Chapter 16: Explainability in AI-Generated Mathematical Proofs

Chapter 17: Bias and Reliability in AI-Based Mathematical Systems

Part V: Emerging Frontiers in AI-Enabled Mathematics

Chapter 18: AI-Assisted Exploration of Open Problems in Mathematics

Chapter 19: Quantum Machine Learning for Advanced Mathematical Computation

Chapter 20: Redefining Mathematical Discovery: A New Paradigm with AI



Important Dates:
Abstract Submission (200 – 300 words) Deadline: September 30th, 2026
Notification of Acceptance: October 15th, 2026
Full Chapter (5,000 – 6,000 words) Submission Due: On or before December 15th, 2026

Submission procedure:
We invite researchers and practitioners to contribute original chapters to this book. Prospective authors are encouraged to submit a one-page chapter proposal or abstract outlining the proposed chapter’s content, objectives, and methodology by September 30th, 2026.

Please include the chapter title and author details within the proposal. Authors will be notified regarding the acceptance of their proposals by October 15th, 2026. Accepted authors will be required to submit completed chapters of 18 to 22 pages by December 15th, 2026. All submitted chapters will undergo a rigorous peer-review process.

To submit your proposal or full-length chapter, please send a Word document attachment to editors: apulnarayan@gmail.com

Authors must refer to the following link for detailed guidelines for chapter preparation:
https://appleacademicpress.com/download/AAP_MS_INSTRUX.pdf


Note: Authors submitting manuscripts to this book do not incur any publication fees. To ensure the originality and quality of the content, all submissions must be previously unpublished and not under consideration for publication in any other venue.


About the Authors / Editors:
Editors: Dr. Ashis Kumar Pati

Ashis Kumar Pati is an academician, researcher, and expert in Machine Learning, Deep Learning, and Artificial Intelligence, with a strong interest in both theoretical foundations and real-world applications of intelligent systems. His research spans diverse areas including computer vision, explainable artificial intelligence (XAI), multimodal learning, generative AI, quantum machine learning, large language models, and healthcare analytics. Pati is actively involved in teaching and mentoring students in machine learning, neural networks, deep learning, data analytics, and statistical learning. He has delivered lectures and technical sessions on data analysis and artificial intelligence for academic and professional audiences. His academic interests also extend to the mathematical foundations of artificial intelligence, including neural networks, optimization, and quantum-inspired learning models. Alongside his research activities, Dr. Pati is engaged in scholarly writing and has contributed to research papers, review articles, and technical publications. His work aims to bridge the gap between theoretical advances in artificial intelligence and their practical implementation across healthcare, business, and industrial domains.

Dr. Apul Narayan Dev

Apul Narayan Dev is currently working as a professor in the Department of Mathematics, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar. He has been working at the institute since January 2016. He did his M. Sc., M.Phil and Ph.D. in Mathematics from Gauhati University in 2009, 2011 and 2016, respectively. He has 15 year of teaching and research experience. His research interests include the theoretical study of plasma, solutions to nonlinear equations, and fluid dynamics. He has published nearly 55 papers in International journals and 5 book chapters in Springer and CRC Press, all of which are either SCI/SCIE or Scopus-indexed. He received an international travel grant from DST, Government of India, in 2017 to visit the 8th ICPDP in Prague, Czech Republic. He has published one book, “Shock Wave Phenomena in Dusty Plasma,” and three books as editor with CRC and DeGruyter. He has also guided three research scholars, and four more students are working under his guidance. As a strength, he has organized four series of International Conferences on Applied Mathematics in Science and Engineering (AMSE-2019, AMSE-2022, AMSE-2024, AMSE-2025) and two International webinars (Maple-2020, Mathematica-2020) and one workshop in 2024. As a leading editor, Dr. Dev also edited a SCOPUS-indexed Conference Proceeding in AIP Publisher, Volume 2819, for AMSE-2023. He is a Guest editor for a special issue in the Journal of Interdisciplinary Mathematics (JIM). He is working on two ongoing book projects as an editor at Elsevier and DeGryter.

Dr. Swadhin Kumar Barisal

Swadhin Kumar Barisal received his Ph. D. from the National Institute of Technology, Rourkela, Odisha, in the year 2022, and his M. Tech. degree in Computer Science and Engineering from IIT Kharagpur, West Bengal, India in the year 2011. He is working as an Associate Professor and coordinator of Centre for CIOT in CSE Department at the Institute of Technical Education and Research (ITER) of Siksha ‘O’ Anusandhan deemed to be University, Odisha. His research interests are in Data Science, Machine Learning, IOT, Software Engineering, Object-Oriented Systems, and Real-Time systems. He has many research publications, including reputed journals with good impact factors and conferences and book chapters. He has supervised many graduates and postgraduates.

Dr. Mukesh Kumar Awasthi

Mukesh Kumar Awasthi has done his Ph.D. on the topic “Viscous Correction for the Potential Flow Analysis of Capillary and Kelvin-Helmholtz instability”. He is working as an Assistant Professor in the Department of Mathematics at BabasahebBhimraoAmbedkar University, Lucknow. Dr. Awasthi is specialized in the mathematical modeling of flow problems. He has taught courses of Fluid Mechanics, Discrete Mathematics, Partial differential equations, Abstract Algebra, Mathematical Methods, and Measure theory to postgraduate students. He has acquired excellent knowledge in the mathematical modeling of flow problems and he can solve these problems analytically as well as numerically. He has a good grasp of the subjects like viscous potential flow, electro-hydrodynamics, magneto-hydrodynamics, heat, and mass transfer. He has excellent communication skills and leadership qualities. He is self-motivated and responds to suggestions in a more convincing manner. Dr. Awasthi has qualified National Eligibility Test (NET) conducted on all India level in the year 2008 by the Council of Scientific and Industrial Research (CSIR) and got Junior Research Fellowship (JRF) and Senior Research Fellowship (SRF) for doing research. He has published 150 plus research publications (journal articles/books/book chapters/conference articles) in Elsevier, Taylor & Francis, Springer, Emerald, World Scientific, and many other national and international journals and conferences. Also, he has published 8 books. He has attended many symposia, workshops, and conferences in mathematics as well as fluid mechanics. He has got the “Research Awards” consecutively four times from 2013-2016 by the University of Petroleum and Energy Studies, Dehradun, India. He has also received the start-up research fund for his project “Nonlinear study of the interface in multilayer fluid system” from UGC, New Delhi. He has been listed in Top 2% World Scientists by Stanford University and Scopus Database.




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