4 papers
C-STRIDE: An Observation-Driven AI Digital Twin for Predicting Basin-Wide Flood Fields from Sparse Stream-Gauge Histories
Yanjie Tong, Phillip Si, Yuan Qiu +1
Emergency managers need to know where floodwater is, how deep it is, and how it will change over the coming hours across an entire river basin. During a flood, however, real-time m…
From Sparse Sensors to Continuous Fields: STRIDE for Spatiotemporal Reconstruction
Yanjie Tong, Peng Chen
Reconstructing high-dimensional spatiotemporal fields from sparse point-sensor measurements is a central challenge in learning parametric PDE dynamics. Existing approaches often st…
Self-Supervised Learning with Noisy Dataset for Rydberg Microwave Sensors Denoising
Zongkai Liu, Qiming Ren, Wenguang Yang +11
We report a self-supervised deep learning framework for Rydberg sensors that enables single-shot noise suppression matching the accuracy of multi-measurement averaging. The framewo…
Time series learning in a many-body Rydberg system with emergent collective amplification
Zongkai Liu, Qiming Ren, Chris Nill +12
Interacting Rydberg atoms constitute a versatile platform for the realization of non-equilibrium states of matter. Close to phase transitions, they respond collectively to external…