Aims & Scope

An international journal, the Journal of Symbolic Computation, founded by Bruno Buchberger in 1985, is directed to mathematicians and computer scientists who have a particular interest in symbolic computation. The journal provides a forum for research in the algorithmic treatment of all types of symbolic objects: objects in formal languages (terms, formulas, programs); algebraic objects (elements in basic number domains, polynomials, residue classes, etc.); and geometrical objects.

It is the explicit goal of the journal to promote the integration of symbolic computation by establishing one common avenue of communication for researchers working in the different subareas. It is also important that the algorithmic achievements of these areas should be made available to the human problem-solver in integrated software systems for symbolic computation. To help this integration, the journal publishes invited tutorial surveys as well as Applications Letters and System Descriptions.

Research Areas Include:

• Computational algebra
• Computational geometry (non-linear)
• Automated theorem proving
• Automatic programming
• Design and implementation of symbolic computation languages and systems
• Applications in education, science, engineering and industry

Special Issue on SCML: Symbolic Computation and Machine Learning
截稿日期: 2027-04-01

This special issue is dedicated to all research that strives to combine "Symbolic Computation" (SC) and "Machine Learning" (ML) as two major approaches to "Artificial Intelligence", in particular: the application of ML to SC, the application of SC to ML, and the hybrid combination of SC and ML to solving problems. Therefore we consider submissions that explore the interaction between the two fields - not standalone works on either SC or ML.

See the web site of the publishing forum “SCML: Symbolic Computation and Machine Learning” (https://scml.risc.jku.at) for examples of research topics that are in the scope of this special issue and for an editorial that describes the general vision of SCML.

A submission to this special issue is typically a substantially extended and revised version of a paper that was previously accepted for the SCML publishing forum and was presented at a virtual SCML workshop (in this case the paper must clearly refer in its introduction to the SCML forum paper on which it is based). The submission is freshly reviewed according to the standards of the Journal of Symbolic Computation.

SCML Steering Committee:

Bruno Buchberger, François Charton, Matthew England, Cezary Kaliszyk, Manuel Kauers, Hiroshi Kera, Temur Kutsia, Bernhard Moser, Markus Schedl, Wolfgang Schreiner, Martina Seidl, Wolfgang Windsteiger.

Guest editors:

Prof. Bruno Buchberger
Research Institute for Symbolic Computation (RISC)
Johannes Kepler University Linz, Linz, Austria
Email: buchberger.bruno@gmail.com 

Prof. Temur Kutsia
Research Institute for Symbolic Computation (RISC)
Johannes Kepler University Linz, Linz, Austria
Email: Temur.Kutsia@risc.jku.at 

Prof. Wolfgang Schreiner
Research Institute for Symbolic Computation (RISC)
Johannes Kepler University Linz, Linz, Austria
Email: Wolfgang.Schreiner@risc.jku.at 

Prof. Wolfgang Windsteiger
Research Institute for Symbolic Computation (RISC)
Johannes Kepler University Linz, Linz, Austria
Email: Wolfgang.Windsteiger@risc.jku.at 

Manuscript submission information:

Important Dates:

    Submission Open Date: July 1, 2025
    Submission Deadline: April 1, 2027
    Notification of Acceptance: July 1, 2027

Contributed full papers must be submitted via the Journal of Symbolic Computation online submission system (Editorial Manager®): Please select the article type “VSI: SCML” when submitting the manuscript online.

Every paper enters the reviewing process immediately after its submission; a decision about its acceptance is made within 3 months.

Please refer to the Guide for Authors to prepare your manuscript.

For any further information, the authors may contact the Guest Editors.

Keywords:

((symbolic computation) OR (computer algebra) OR (automated reasoning) OR (program synthesis)) AND
((machine learning) OR (large language models))