Date: 15th August (afternoon), Bremen, Germany
Make sure to download the jupyter notebook and data files before the tutorial or make sure you can open them on Google Colab!
This tutorial offers an accessible but comprehensive journey into the emerging intersection of neural networks and differential equations, two pillars at the core of scientific modelling and modern AI. Beginning with an introduction to the theory and motivation behind differential equations, we build toward the conceptual and algorithmic foundations of architectures that integrate them with neural networks. Participants will develop a clear understanding of how approaches like Physics-Informed Neural Networks (PINNs), Neural Operators, and Neural Ordinary Differential Equations (Neural ODEs) are transforming modelling, simulation, and data-driven discovery across scientific domains.
Target Audience: from first-year PhD students to experienced researchers.