6 papers
Spatiotemporal Imputation with Graph-Informed Flow Matching
Zepeng Zhang, Aref Einizade, Jhony H. Giraldo +1
Missing data is a common challenge in spatiotemporal systems, arising in applications such as air quality monitoring and urban traffic management. Traditional machine learning appr…
RINS-T: Robust Implicit Neural Solvers for Time Series Linear Inverse Problems
Keivan Faghih Niresi, Zepeng Zhang, Olga Fink
Time series data are often affected by various forms of corruption, such as missing values, noise, and outliers, which pose significant challenges for tasks such as forecasting and…
GeoLink: Empowering Remote Sensing Foundation Model with OpenStreetMap Data
Lubian Bai, Xiuyuan Zhang, Siqi Zhang +4
Integrating ground-level geospatial data with rich geographic context, like OpenStreetMap (OSM), into remote sensing (RS) foundation models (FMs) is essential for advancing geospat…
From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM
Olga Fink, Ismail Nejjar, Vinay Sharma +13
Prognostics and Health Management ensures the reliability, safety, and efficiency of complex engineered systems by enabling fault detection, anticipating equipment failures, and op…
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…