This webpage is dedicated to research on Physics-Informed Neural Networks at KTH, Sweden

We are a group of PINN enthusiasts from different scientific fields working at KTH, Sweden, who have a joint interest in progressing scientific knowledge on PINN applications.

Physics-Informed Neural Networks (PINNs)

- Artificial neural networks (ANNs) that use prior knowledge stored in partial differential equations (PDEs).

- PINNs constrain the outputs of the ANN to a physical model expressed by the PDE.

>>One of our projects<<

PINNs for transformer thermal modelling

Physics-Informed Neural Networks (PINNs) is a new method in machine learning addressed to solve and identify nonlinear partial differential equations (PDEs). PINNs rely on neural networks (NN) capability to approximate any function and combine this property with the physics outlined in nonlinear PDEs.

Dynamic thermal modelling for power transformers plays a fundamental role in studying their thermal behaviour and ensuring their efficiency and longevity. Conventional dynamic thermal models include the IEC and the IEEE standards however, one of the main disadvantages of these models is that they cannot provide the thermal distribution.

This project focuses on power transformers thermal modelling using PINNs to solve the one-dimensional heat diffusion equation. The aim is to predict the top-oil temperature while also estimating the thermal distribution inside the transformer. The Finite Volume Method (FVM) is utilised to calculate the PDE solution and to benchmark the PINNs predictions. Field measurements taken from a real transformer, including the top-oil temperature, the ambient temperature and the load factor, will guarantee a model very close to the real world.

PINN for modelling 1D heat diffusion equation

Finite Volume Method (FVM) vs PINN