Advances in Neural Networks and Their Applications

Dear Colleagues,

In recent years, we have experienced tremendous change in the field of neural networks driven by groundbreaking innovations in Large Language Models (LLMs).

In fact, these models, along with other cutting-edge neural architectures, are redefining our ability to process complex data. This, in turn, allows us to face new, unprecedented challenges. From self-driving cars to digital transformations in business and industry, neural networks have become the cornerstone of various innovations.

This Special Issue of Mathematics aims to collect the most recent discoveries in the field of neural networks, with particular emphasis on theoretical foundations, algorithmic innovations, and advanced applications. The objective is to explore the intersection between mathematics and neural network research, highlighting both fundamental progress and practical implementations that address real-world challenges.

Topics of interest include, but are not limited to, the following:

• Innovations in neural network architectures, including Large Language Models (LLMs);
• Theoretical advancements in understanding the performance and limitations of LLMs;
• Techniques for efficiently training and fine-tuning large-scale models;
• Applications of neural networks in natural language processing, computer vision, and multimodal learning;
• Data preprocessing, augmentation, and integration for large-scale neural networks;
• Explainable AI and techniques for interpreting complex models;
• Robustness and generalization;
• Reinforcement learning and multi-agent systems;
• Autonomous systems and intelligent agents for decision-making and control;
• Applications of neural networks in computational biology, social sciences, and financial systems;
• Domain adaptation, transfer learning, and cross-domain applications;
• Novel evaluation metrics for neural networks;
• Mathematical modeling of neural network dynamics and optimization processes;
• AI-based tools for automation, simulation, and digital transformation.

Dr. Giorgia Minello
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• multimodal learning
• explainable AI
• mathematical modeling
• robustness and generalization
• large language models (LLMs)
• deep learning architectures
• reinforcement learning
• transfer learning