Topics

Authors are strongly encouraged to present papers that combine and blend physical knowledge and artificial intelligence/machine learning algorithms. Topics of interest include but are not limited to the following:

• Artificial intelligence/machine learning framework that can seamlessly synthesize models, governing equations and data.

• Approaches to encode scientific knowledge in machine learning method and architecture

• Architectural and algorithmic improvements for scalable physics-informed learning

• Stability and error analysis for physics-informed learning

• Software development facilitating the inclusion of physics domain knowledge in learning

• Discovery of physically interpretable laws from data

• Applications incorporating domain knowledge into machine learning

We solicit extended abstracts, full papers, and poster abstracts on topics related to the above and can include recent or ongoing research, surveys, and business/use cases.

• Extended abstracts (2 to 4 pages) and full papers (up to 6 pages) will be peer-reviewed. References do not count towards the page limit.

• Posters can be proposed by submitting an abstract (1 to 2 pages).

All submissions should follow the AAAI format in the Author Kit, will be handled through EasyChair and the review will be double-blind to ensure academic integrity.

AAAI no longer handles proceedings and authors can replace the copyright in the Author Kit with

Copyright ©2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

We will update the copyright (CC BY 4.0) in the accepted manuscripts before final publication.

Accepted extended abstracts and full papers shall be published in the open access proceedings site CEUR-WS.