Generative AI: Advanced Technologies, Applications, and Impacts

A special issue of Sci (ISSN 2413-4155). This special issue belongs to the section "Computer Sciences, Mathematics and AI".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 695

Special Issue Editors


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Guest Editor
Division of Bioorganic Chemistry, School of Pharmacy, Saarland University, D-66123 Saarbruecken, Germany
Interests: bioorganic chemistry; catalytic sensor/effector agents; epistemology; intracellular diagnostics; nanotechnology; natural products; reactive sulfur and selenium species; redox regulation via the cellular thiolstat
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer Science & Engineering (DISI), University of Bologna, 40136 Bologna, Italy
Interests: wireless sensor and actuator networks; middleware for sensor and actuator networks; vehicular sensor networks; edge computing; fog computing; online stream processing of sensing dataflows; IoT and big data processing; pervasive and mobile computing; cooperative networking; cyber physical systems for Industry 4.0
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Computer Science, University of St Andrews, St Andrews KY16 9SX, UK
Interests: computer vision; pattern recognition; machine learning; bioinformatics statistics mathematical modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The recent successes and the widespread popularity of Generative AI have attracted growing interest and opened up new opportunities, not only in terms of the exploitation of Generative AI tools in several application domains, but also in terms of its novel scientific/technological challenges, e.g., making it more decentralized and edge-based in cloud continuum deployment environments. As a notable example, Generative AI models such as Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) are capable of creating high-quality content in diverse modes (including text, images, videos, etc.) and improving/accelerating several research/business tasks in many sectors, such as via dataset augmentation for simulations and digital twins. These capabilities bring enormous opportunities to a wide range of research areas, for both basic and applied research. Actual applications range from humanities and social sciences to pharmacy and medicine and include innovative AI-driven drug design and healthcare.

This Special Issue aims to provide a fresh and up-to-date publication venue for researchers and practitioners to share novel and high-quality contributions related to all aspects of Generative AI, from associated technologies and advancements to novel fields of applications and related tools and lessons learned from performance evaluation and experiences over large-scale testbeds.

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

  • Generative AI for digital twinning;
  • Generative AI for edge computing and edge-based Generative AI;
  • Metaverse applications exploiting Generative AI;
  • Generative AI under runtime and dynamic quality (e.g., time latency) constraints;
  • Internet and B5G infrastructure support for Generative AI;
  • Privacy and security of Generative AI impactive federated learning and trusted execution environments;
  • Benchmarking Generative AI solutions in different scientific and application domains, including AI-guided drug design and healthcare;
  • Generative AI’s use of software tools.
  • Applications and case studies. 

Prof. Dr. Claus Jacob
Prof. Dr. Paolo Bellavista
Dr. Ognjen Arandjelović
Guest Editors

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Keywords

  • generative AI
  • digital twins
  • edge computing
  • cloud continuum
  • AI in medicine

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Published Papers (1 paper)

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Research

14 pages, 278 KB  
Article
Transformers and State-Space Models: Fine-Tuning Techniques for Solving Differential Equations
by Vera Ignatenko, Anton Surkov, Vladimir Zakharov and Sergei Koltcov
Sci 2025, 7(3), 130; https://doi.org/10.3390/sci7030130 - 11 Sep 2025
Viewed by 347
Abstract
Large language models (LLMs) have recently demonstrated remarkable capabilities in natural language processing, mathematical reasoning, and code generation. However, their potential for solving differential equations—fundamental to applied mathematics, physics, and engineering—remains insufficiently explored. For the first time, we applied LLMs as translators from [...] Read more.
Large language models (LLMs) have recently demonstrated remarkable capabilities in natural language processing, mathematical reasoning, and code generation. However, their potential for solving differential equations—fundamental to applied mathematics, physics, and engineering—remains insufficiently explored. For the first time, we applied LLMs as translators from the textual form of an equation into the textual representation of its analytical solution for a broad class of equations. More precisely, we introduced a benchmark and fine-tuning protocol for differential equation solving with pre-trained LLMs. We curated a dataset of 300,000 differential equations and corresponding solutions to fine-tune T5-small, Phi-4-mini, DeepSeek-R1-Distill-Qwen, and two Mamba variants (130M and 2.8B parameters). Performance was evaluated using BLEU and TeXBLEU metrics. Phi-4-mini achieved the best results, with average BLEU > 0.9 and TeXBLEU > 0.78 across all considered equation classes, which shows the strong generalization abilities of the model. Therefore, this model should be further investigated on a broader class of differential equations and potentially can be used as a part of mathematical agents for solving more complex particular tasks, for example, from physics or engineering. Based on our results, DeepSeek-R1-Distill-Qwen consistently underperformed, while T5 showed strong results for the most frequent equation type but degraded on less common ones. Mamba models achieved the highest TeXBLEU scores despite relatively low BLEU, attributable to their production of lengthy outputs mixing correct expressions with irrelevant ones. Full article
(This article belongs to the Special Issue Generative AI: Advanced Technologies, Applications, and Impacts)
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