The Interdisciplinary Application of Artificial Intelligence: From Models to Explainability
A Special Issue of Applied Sciences (ISSN 2076-3417) belonging to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 February 2027 | Viewed by 108
Editor
Interests: distributed artificial intelligence; machine learning; virtualisation; analog computing; computer engineering; material-integrated intelligent systems; HW/SW co-design, system-on-chip design and HLS software; hardware and embedded system design; tiny ML; virtualization on chip; mixed analog/digital; sensor networks; multi-agent systems and platforms; simulation research; structural monitoring and damage diagnostics; crowd sensing and data mining
Special Issue Information
Dear Colleagues,
Artificial Intelligence (AI) covers a broad range of methods and models, including data-driven Machine Learning (ML). AI is deployed today in nearly all scientific disciplines. On the one hand, ML is often considered on a mathematical and theoretical level, ignoring reality. On the other hand, AI and ML is used on a practical level without deeper understanding of the models. This Special Issue aims to narrow the gap between theoretical and practical aspects, and scientists from various disciplines are welcome to present their research and the challenges and opportunities of Artificial Intelligence. Natural and engineering sciences rely on physical and analytical laws; AI is not aware of any kind of causality and laws. The interdisciplinary application of Artificial Intelligence can help to bring explainability, interpretation, and tractability to AI/ML methods and models. The affected disciplines range from natural science, social science, and medicine to engineering and computer science. How algorithms influence public communication, the interaction between art and AI, the use of chatbots and AI in physics and school, AI-assisted analysis in medicine and material sciences, polyglot machines, understanding how machines learn, how AI is changing science, and many more topics explore the frontiers of knowledge answering the question of what AI can really do. Explainability and interpretability of AI/ML models are only possible by a union of multiple disciplines.
Prof. Dr. Stefan Bosse
Guest Editor
Manuscript Submission Information
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Keywords
- artificial intelligence (AI)
- machine learning (ML)
- explainable artificial intelligence (XAI)
- explainability and interpretability
- interdisciplinary research
- AI-assisted analysis
- intelligent algorithms
- AI application in science
- physics-informed ML
- model-based ML
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