2 citations · 2 across the 4 of their papers we have counts for
5 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…
Toward AI-Driven Digital Twins for Metropolitan Floods: A Conditional Latent Dynamics Network Surrogate of the Shallow Water Equations
Phillip Si, Yuan Qiu, Omar Sallam +4
AI-driven flood digital twins demand fast hydrodynamic surrogates for ensemble forecasting and observation assimilation. Yet even GPU-accelerated two-dimensional shallow water equa…
Interpretable Battery Aging without Extra Tests via Neural-Assisted Physics-based Modelling
Yuan Qiu, Wei Li, Wei Zhang +4
State of health (SoH) is widely used for battery management, but it is a single scalar and offers limited interpretability. Two batteries with similar SoH can exhibit very differen…
Variationally correct operator learning: Reduced basis neural operator with a posteriori error estimation
Yuan Qiu, Wolfgang Dahmen, Peng Chen
Minimizing PDE-residual losses is a common strategy to promote physical consistency in neural operators. However, standard formulations often lack variational correctness, meaning…
Derivative-enhanced Deep Operator Network
Yuan Qiu, Nolan Bridges, Peng Chen
The deep operator networks (DeepONet), a class of neural operators that learn mappings between function spaces, have recently been developed as surrogate models for parametric part…