Using Large Language Models for Scientific Problem Solving and Engineering Design
A special issue of Machine Learning and Knowledge Extraction (ISSN 2504-4990). This special issue belongs to the section "Learning".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 650
Special Issue Editors
Interests: electronic design automation; machine learning; design creativity; cyber-social systems
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; graph theory applications
Special Issue Information
Dear Colleagues,
This Special Issue is dedicated to papers presenting overviews and state-of-the-art methods that use Large Language Models (LLMs) for scientific problem solving and engineering design. LLMs offer several intriguing, new capabilities, like content creation, summarization, question answering, translation, interfacing to human language, and so on. Still, problem solving and design automation also involve specific activities, which require devising more effective techniques for problem framing, solution partitioning, creating correct and optimized implementations (designs), addressing a broad set of constraints, incorporating human preferences, to name a few. We encourage interdisciplinary submissions that bridge machine learning, electronic design automation, design science, and cognitive science, but any work discussing using LLMs for problem solving and design is of interest.
Prof. Dr. Alex Doboli
Prof. Dr. K. Wendy Tang
Prof. Dr. Simona Doboli
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
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. Machine Learning and Knowledge Extraction is an international peer-reviewed open access monthly 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 1800 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.
Keywords
- large language models
- problem solving
- automated engineering design
- agents
- prompting
- reinforcement learning
- retrieval-augmented generation
- symbolic machine learning
- knowledge representations
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