PAV-IA

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.

The registration is free of charge but it is mandatory on the workshop website.