Welcome to 2023 PhD Summer School

"Physics-Informed Neural Networks and Applications" 

When & Where

19-30th of June 2023

KTH Royal Institute of Technology

Stockholm, Sweden


The course can be followed as:

- a full version, intended for PhD students

- a short option (only includes lectures), intended for researchers from academia and industry.

Target audience: researchers in engineering and applied disciplines

Participation fee:

- Academic* 150 EUR

- Industry 700 EUR

*KTH participants are eligible for a course fee waiver please contact organizers directly for more details. 

To sign up follow the link


Due to extensive interest we decided to set up final deadline for expression of interest for participation. 

If you are interested in attending the program please fill in the expression of interest form before

5th of January 1 PM CET

Guest Lecturers

George Em Karniadakis

The Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and Engineering, Brown University and MIT & PNNL

Khemraj Shukla

Assistant Professor of Applied Mathematics, Brown University

The course syllabus is adapted for participants from engineering disciplines and is focused on providing practical guidance towards the application of PINNs and Deep Learning to problems in engineering research disciplines.

Participants should be aware that the course target group is PhD students and researchers in engineering disciplines. We encourage PhD students who will be taking a full version of the course to propose projects that are related to their research.


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 on Sunday the 18th of June.

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

5. Participants of the full course version are required to prepare a short 5-minute introductory presentation to their research work. 

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

Project scope overview

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


KTH Digitalization Platform

Marco Laudato

Amritam Das

Federica Bragone

Kateryna Morozovska

Karl Henrik Johansson

Contact organizers

For general inquiries


For registered participants