Dates: Sat, Sept. 26 & Sun, Sept. 27
Location: Jordan Hall of Science
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.
View Schedule: https://sciml26nd.github.io/schedule.html
Organizers
- Zecheng Zhang, Daniele Schiavazzi, Zhiliang Xu — University of Notre Dame
- Guang Lin, Di Qi — Purdue University