About the Conference.

In recent years, the combination of deep learning techniques and numerical methods has gained increasing interest as a research area in applied mathematics, especially in the approximation of PDEs. One promising paradigm that has emerged is operator learning. These innovative methodologies are crucial since traditional numerical methods face significant challenges with complex, high-dimensional PDEs that make them unfeasible for real-time simulation in real-world applications.

The goal of this workshop is to bring together researchers and practitioners who are currently working on this cutting-edge topic, fostering collaboration and knowledge exchange in this rapidly evolving field.

This conference will feature keynote presentations from distinguished French mathematicians, made possible thanks to the collaborative project Cassini Junior and the Department of Mathematics at the University of Pavia. This international partnership enables us to present world-class research and establish new connections across borders.

Whether you’re a seasoned researcher, an emerging scholar, or a practitioner in the field, this conference offers valuable insights into the latest developments in operator learning and provides an excellent platform to connect with the global mathematical community.