Partial Differential Equations (PDEs) form the cornerstone of mathematical modeling in mechanics and the natural sciences, driving advances in analysis, numerical methods, and applied mathematics. Today, the rise of Machine Learning (ML) and Artificial Intelligence (AI) presents transformative opportunities and challenges for classical PDE methodologies. Can ML enhance PDE techniques without sacrificing mathematical rigor? Can we develop hybrid computational frameworks that leverage data-driven approaches while maintaining the reliability of traditional methods?
Description
This workshop is focused on the theoretical and practical approach to the use of machine learning methods in the study of partial differential equations. It is aimed at advanced undergraduate and graduate students, as well as academics with expertise in related fields. Activities will include courses, practical sessions, and in-person conferences.
Confirmed Invited speakers
- Enrique Zuazua, Friedrich Alexander Universität Erlangen Nürnberg
- Stephen Wright, Wisconsin Institute for Discover, University of Wisconsin-Madison.
- Víctor Hernández Santamaría, Instituto de Matemáticas, Universidad Nacional Autónoma de México
- Juan Daniel Meshir Vargas, Universidad de Guadalajara
- Subrata Majumdar, Instituto de Matemáticas, Universidad Nacional Autónoma de México
- Oscar Dalmau Cedeño, Cimat
- María de la Luz Jimena de Teresa, IMATE,UNAM
Organizing committee
María de la Luz Jimena de Teresa, IMATE,UNAM
Oscar Dalmau Cedeño, Cimat
Silvia Jerez Galiano, Cimat
