5 papers
Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics
Amaury Wei, Olga Fink
Rigid body interactions are fundamental to numerous scientific disciplines, but remain challenging to simulate due to their abrupt nonlinear nature and sensitivity to complex, ofte…
Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling
Leandro Von Krannichfeldt, Kristina Orehounig, Olga Fink
Building energy modeling is a key tool for optimizing the performance of building energy systems. Historically, a wide spectrum of methods has been explored -- ranging from convent…
Combining Physics-based and Data-driven Modeling for Building Energy Systems
Leandro Von Krannichfeldt, Kristina Orehounig, Olga Fink
Building energy modeling plays a vital role in optimizing the operation of building energy systems by providing accurate predictions of the building's real-world conditions. In thi…
Domain Adaptive Unfolded Graph Neural Networks
Zepeng Zhang, Olga Fink
Over the last decade, graph neural networks (GNNs) have made significant progress in numerous graph machine learning tasks. In real-world applications, where domain shifts occur an…
Algorithm-Informed Graph Neural Networks for Leakage Detection and Localization in Water Distribution Networks
Zepeng Zhang, Olga Fink
Detecting and localizing leakages is a significant challenge for the efficient and sustainable management of water distribution networks (WDN). Leveraging the inherent graph struct…