Scope

Deadline Date: 28 February 2026

The technique of making a computer, a robot controlled by a computer, or software think intellectually like the human mind is recognized as artificial intelligence. AI is achieved by examining the cognitive process and the patterns found in the human brain. Intelligent software and systems are developed as a result of these studies. Last but not least, AI-based mathematical modeling is a relatively new technology that solves challenging physics or biology issues more precisely than ever before by fusing deep learning algorithms with more conventional techniques like linear regression or neural networks. Calculus helps AI systems learn and get better over time by reducing errors. Statistics and Probability: Data is the foundation of AI, yet it is rarely flawless. AI can deal with uncertainty thanks to probability and statistics, which enable it to make defensible conclusions even in the face of inadequate data. A real-world problem is transformed into a mathematically well-posed problem by mathematical modeling, which is an iterative, cyclical process that is then analyzed mathematically and its answer interpreted in terms of real-world restrictions.

Quantities that rise or fall in accordance with an exponential curve are described by models of exponential growth and exponential decay. Analytical thinking, a critical ability in AI, can be improved by mathematics. Despite popular belief, artificial intelligence is not magic. The magic underlying these inventions comes from mathematics. Finding patterns and correlations in data is essential for activities like anomaly detection, picture recognition, and natural language processing. Applications of mathematics include measurement, statistics, and personal finance, bringing mathematics into the real world. Students gain the knowledge, comprehension, and abilities necessary to apply mathematical concepts and techniques in practical settings. It helps us anticipate future events, make well-informed judgments, and simplify and comprehend complicated systems. The many uses of mathematical modeling, ranging from managing pandemics to space exploration, highlight how essential it is to expanding knowledge and solving global issues.

Numerical constants and variables that represent various system components are frequently included in mathematical models. The kinematic equations from physics, for instance, can be used to explain how a baseball moves through the air after being pitched. The process of creating an abstract model in mathematical terms to explain the intricate behavior of an actual system is recognized as mathematical modeling. Ordinary differential equations and partial differential equations are frequently used to express mathematical models, which are quantitative models. A good mathematical model should be straightforward, accurate, and useful in practical settings. One of the most important qualities of a good mathematical model is accuracy. A high level of precision in result prediction should be possible using the model. Learn how evolutionary computation models the real world and identifies the most effective, efficient, lowest cost, and/or highest revenue producing outcomes, so helping to solve challenges that human decision making may overlook. Artificial intelligence models are its virtual brains. An algorithm becomes an AI model after it has been trained using data. The accuracy of the model increases with the amount of data it contains. Machine learning, supervised learning, unsupervised learning, and deep learning are a few of the several kinds of AI models. We invite submissions from across disciplines and perspectives, without being confined to specific domains: Mathematical Modeling and Theory in the Evolution of Artificial Intelligence.

List of topics relevant to this special issue include but are not limited to:

• Neural Networks' Mathematical Underpinnings: Progress in Comprehending Deep Learning Architectures.
• AI Optimization Algorithms: Conceptual Understanding and Real-World Uses.
• Bayesian techniques and probabilistic models in the development of artificial intelligence.
• Generalized Learning Algorithms in AI Systems: Mathematical Theories.
• Artificial Intelligence: Using Game Theory to Model Strategic Decision-Making.
• Tensor Calculus and Matrix Factorization: The Function of Linear Algebra in Contemporary AI.
• AI behavior modeling using differential equations and dynamical systems.
• Applications of Graph Theory to Artificial Intelligence: Progress in Knowledge Representation.
• Mathematical Models for AI Reliability and Robustness in the Face of Uncertainty.
• Investigating Spaces of Solutions for Optimization Issues through Functional Analysis in AI.
• Complexity Theory in Artificial Intelligence: Assessing Scalability and Algorithmic Efficiency.
• AI Using Symbolic Mathematics to Model Reasoning and Logic Mechanisms.