AiMTT has published a new tutorial in its AI in Mobility series. In this article, Ying-Chuan Ni from ETH Zurich and Theivaprakasham Hari from TU Delft explain what Physics-Informed Neural Networks (PINNs) are and why they matter.
The relatively new Physics-Informed Neural Networks combine the strengths of traditional traffic flow theory models with those of data-driven machine learning models. How do they work? What are their potential applications? And what challenges remain? In this tutorial, the authors explain in clear, accessible language what PINNs can offer the field of traffic and transportation.
The PINN tutorial is available in our Tutorials section. A Dutch translation has also been published in NM Magazine.
In Partnership with NM Magazine
The translation and publication of the article in NM Magazine are the result of the partnership between AiMTT and NM Magazine. As a leading Dutch trade journal for mobility professionals, NM Magazine plays an important role in disseminating the knowledge developed within AiMTT.