The summer school is organized around five topics, each topic corresponding to one course. The format of each course is 4 hours of lectures and 2 hours of computer sessions (CS) on Jupyter Notebooks with Python or Julia (in the case of a more theoretical topic, one or two focused talks will complement the main lecture). In the computer session, the goal would be to implement with the participants an elementary working example that could potentially be of help later on in the projects. The lectures range from theoretical (approximation theory) to applied (solution of PDEs, forward and inverse problems) up to and including real-world applications. Please visit the registration page for more information on registration, accommodation, etc. Note that in-person spots are no longer available, only fully remote registration («hybrid mode» on the registration website) is possible.
Linear and Nonlinear Schemes for Forward Model Reduction and Inverse Problems
Finite Neuron Method
Learning Operators
Data-Driven Latent Representations for Time-Dependent Problems
Towards data-driven high fidelity CFD