Physics-Informed Graph Neural Networks for Water Distribution Systems

1 code implementation • 27 Mar 2024 • Inaam Ashraf, Janine Strotherm, Luca Hermes, Barbara Hammer

In this realm, we propose a novel and efficient machine learning emulator, more precisely, a physics-informed deep learning (DL) model, for hydraulic state estimation in WDS.

Spatial Graph Convolution Neural Networks for Water Distribution Systems

1 code implementation • 17 Nov 2022 • Inaam Ashraf, Luca Hermes, André Artelt, Barbara Hammer

We investigate the task of missing value estimation in graphs as given by water distribution systems (WDS) based on sparse signals as a representative machine learning challenge in the domain of critical infrastructure.

Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning

1 code implementation • 4 Jul 2022 • Christian Löwens, Inaam Ashraf, Alexander Gembus, Genesis Cuizon, Jonas K. Falkner, Lars Schmidt-Thieme

In this work, we generalize this concept of heterogeneous attentions to the TSPPC.

reinforcement-learning Reinforcement Learning (RL)