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
Registration is closed
Summer School Information
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.
Prerequisites
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
Directions
KTH Campus Map
The main KTH Campus is situated in the northern part of central Stockholm, on the edge of the Royal National Park.
Key locations
Digital Futures or DF
Osquars backe 5, Stockholm,
Architecture building KTH,
1st floor.
For registration on Sunday, June 18th, project work, Q&A sessions, all activities for week 2; welcome reception and other non-program activities.
map position
F2
Lindstedtsvagen 26,28. For late registration on Monday, June 19th. For Lectures 1-8 during week 1.
Hugin/Munin or H/M:
Teknikringen 8.
For Python Primer on Sunday, June 18th and Lectures 9 & 10 during week 1.
Travel in Stockholm
Public transport operates under one ticket system, covering: subway, trains, trams, busses and even several ferry connections.
To use public transport you should purchase your ticket using Sl.se look for respective applications for android and iPhone.
Ticket can also be purchased at the chasier in the subway station or at the train station or you can pay directly with your VISA or MasterCard by using it to enter any of the abovementioned transports.
Airport
If possible, we recommend that you fly into Arlanda airport, which is the main international airport for the Stockholm region. Additionally, there are two other airports in the area: Bromma airport, a smaller city airport, and Skavsta airport, which is located farther out of town and mainly serves Ryan Air flights.
To reach the town from the airport you can use the following serives:
Arrive to KTH campus
You can reach the principal campus of KTH from the main train station getting to the station called Tekniska Högskolan by subway.
Once to the KTH main campus, you can refer to campus map to find the PINNs summer school facilities.
Sponsors
KTH Digitalization Platform
Marco Laudato
Amritam Das
Federica Bragone
Kateryna Morozovska
Karl Henrik Johansson
Contact organizers
For general inquiries
info@pinns.se
For registered participants
2023@pinns.se
Teacher Assistants
Nicole Rosi
Lorenzo Branca
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