8-14th of July 2024 Lectures (in-person)

15-23th of July 2024 Project work (hybrid)

KTH Royal Institute of Technology

Stockholm, Sweden

Registration is closed 

Accepted participants will be notified soon

Due to limited funds we are only able to offer up to 30 spots. 

You will be informed shortly after the deadline if your application was selected.

Prerequisites & credits

1. Knowledge of at least one programming language. 

2. It is preferable that course participants have a working knowledge of Python*

*For those who are less familiar with Python a primer on Python, NumPy, SciPy, and Jupyter Notebooks will be taught during the program

4. We encourage participants of the project work to prepare datasets they can use.

5. The course lectures will be credited as 2 ECTS. If you wish to do course project (can be both in person or hybrid) the final number of credits is 5 ECTS after successful project presentation and reporting.

Lecture overview

The course syllabus will cover a variety of topics:

- Introduction to Deep Learning Networks

- Neural Network 

- TensorFlow, PyTorch, JAX

- Discovery of differential equations

- Physics-Informed Neural Networks (advanced)

- DeepONet

- {DeepXDE} or {MODULUS}

- Uncertainty quantification

- Multi-GPU machine learning

We encourage course participants to formulate projects related to their area of research.

Additional project topics will be provided for selection.

Examples of project areas

- Biomedicine:  Modelling blood coagulation

- Control: System identification and decision making

- Dynamical Systems: Charged particles in the electromagnetic field

- Engines: Learning engine parameters

- Fluid Mechanics: Bubble growth dynamics

- Geophysics: Diffusion-reaction in porous media

- Heat Transfer: Non-linear Inverse heat conduction problem

- Materials: Modulus identification of hyperelastic material