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.
• Algorithms for scalable physics-informed learning
• Stability and error analysis for physics-informed learning
• Software development facilitating the inclusion of physics domain knowledge in learning
• 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.
• 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 (https://easychair.org/conferences/?conf=sss20) and the review will be double-blind to ensure academic integrity.

AAAI no longer handles proceedings and authors can remove the copyright in the Author Kit. We will remove the copyright in the manuscripts before final publication.

Accepted extended abstracts and full papers shall be published in an open access proceedings site.