Journal Information

Advanced Engineering Informatics (AEI)

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Impact Factor:
9.9
Publisher:
Elsevier
ISSN:
1474-0346
Viewed:
41830
Tracked:
21

Call For Papers

Advanced Engineering Informatics (AEI) is an academic journal published by Elsevier. (ISSN 1474-0346, impact factor 9.9, CCF B).

The science of supporting knowledge-intensive activities Advanced computing methods and related technologies are changing the way engineers interact with the information infrastructure. Explicit knowledge representation formalisms and new reasoning techniques are no longer the sole territory of computer science. For knowledge-intensive tasks in engineering, a new philosophy and body of knowledge called Engineering Informatics is emerging. Advanced Engineering Informatics solicits research papers with particular emphases both on 'knowledge' and 'engineering applications'. As an international Journal, original papers typically: • Report progress in the engineering discipline of applying methods of engineering informatics. • Have engineering relevance and help provide the scientific base to make engineering decision-making more reliable, spontaneous and creative. • Contain novel research that demonstrates the science of supporting knowledge-intensive engineering tasks. • Validate the generality, power and scalability of new methods through vigorous evaluation, preferably both qualitatively and quantitatively. In addition, the Journal welcomes high quality review articles that summarise, compare, and evaluate methodologies and representations that are proposed for the field of engineering informatics. Similarly, summaries and comparisons of full-scale applications are welcomed, particularly those where scientific shortcomings have hindered success. Typically, such papers have expanded literature reviews and discussion of findings that reflect mastery of the current body of knowledge and propose novel additions to contemporary research. Papers missing explicit representation and use of knowledge, such as those describing soft computing techniques, mathematical optimization methods, pattern recognition techniques, and numerical computation methods, do not normally qualify for publication in the Journal. Papers must illustrate contributions using examples of automating and supporting knowledge intensive tasks in artifacts-centered engineering fields such as mechanical, manufacturing, architecture, civil, electrical, transportation, environmental, and chemical engineering. Papers that report application of an established method to a new engineering subdomain will qualify only if they convincingly demonstrate noteworthy new power, generality or scalability in comparison with previously reported validation results. Finally, papers that discuss software engineering issues only are not in the scope of this journal.
Last updated by Dou Sun in

Special Issues

Special Issue on Application of Large Language Models in Energy Engineering and Informatics Submission Date: 2026-07-31 Large language modeling (LLM), as one of the hottest research areas in artificial intelligence (AI), has demonstrated its powerful impact in many research areas. With the rapid advancements in LLMs, their applications have extended well beyond natural language processing into diverse scientific and engineering domains. In the context of modern energy systems, LLMs offer exciting new opportunities to address long-standing challenges. This special issue aims to provide an international forum for researchers to exchange up-to-date outcomes on AI, LLM and energy engineering (EE) to address the energy engineering and informatics issues in real-world scenarios. These three exciting research areas (AI, LLM and EE) have attracted extensive research interests over last decades, both from the science research community and the engineering research group. With the emergence of novel methods and systems, recent progresses remain to be investigated and studied. Therefore, a special issue is proposed to satisfy this requirement, which will have a great significance and profound impact on the next-generation energy design, including net-zero green building design, smart cities developments with AI, intelligent system control for net-zero energy design and so on. AI and large language modeling for modern energy systems. Intelligent control using large language models for energy systems. Large language models for energy consumption pattern recognition and forecasting. Large language models for sustainable energy generation forecasting. Large language models for energy balance design in smart and green buildings. Large language model empowered physics modeling in energy systems. Big data analysis for sustainable energy design with large language models. Physics informed AI models for sustainable energy design applications. Guest editors: Prof. Ke Yan Hunan University, Changsha, China Dr. Vincent Gan National University of Singapore, Singapore City, Singapore Prof. Amy Trappey National Tsing Hua University, Hsinchu, Taiwan Prof. Fu Xiao The Hong Kong Polytechnic University, Hong Kong, Hong Kong
Last updated by Dou Sun in

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