Workshop Scope
Recent developments in artificial intelligence and advanced machine learning tools are introducing new insights and approaches for advancing research across a wide variety of disciplines.
New mathematical theories and practically applicable strategies are urgently needed. These call for a deeper understanding of machine learning methods, in order to model crucial yet poorly represented processes and to improve our ability to understand and predict both natural and artificial phenomena.
Significant progress has been made on these questions from several perspectives, including rigorous mathematical theory, quantitative and qualitative modeling, and novel multiscale asymptotic and computational strategies.
This workshop brings together researchers and practitioners from diverse backgrounds to exchange ideas and share recent advances in the theoretical, computational, and applied aspects of scientific machine learning. Recent developments in digital twin technology, agentic AI workflows, and their combination are also of strong interest.
Organizers
- Zecheng Zhang, Daniele Schiavazzi, Zhiliang Xu — University of Notre Dame
- Guang Lin, Di Qi — Purdue University
Speakers
Confirmed Speakers (in alphabetical order)
Support
This workshop is supported by the National Science Foundation under Grant No. 2615564, through the Computational Mathematics Program (Program Officer: Jodi Mead). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
This workshop is also supported thanks to the generosity of the following organization at the University of Notre Dame:
- Department of Applied and Computational Mathematics and Statistics
- The College of Science and the College of Engineering
- Notre Dame Research
- The Scientific AI Center
- The Data, AI and Computing (DAC) Initiative