2 citations · 10 across the 17 of their papers we have counts for
6 papers · 1 filter
Knowledge-Graph-Augmented Chronos-2 for HEC-RAS Surrogate Forecasting
Edward Holmberg, Elias Ioup, Mahdi Abdelguerfi
We investigate whether coupling a time-series foundation model to hydraulic project knowledge improves surrogate forecasting of HEC-RAS water-surface elevation (WSE). We present KG…
Learned Response-Field Inertia Operator for HEC-RAS 2D Water-Surface Elevation Prediction
Edward Holmberg, Elias Ioup, Md Meftahul Ferdaus +2
This article presents a cross-dataset evaluation of learned native-cell surrogate models for solver-consistent water-surface elevation (WSE) prediction in HEC-RAS 2D. To avoid rast…
Bridging Spectral Operator Learning and U-Net Hierarchies: SpectraNet for Stable Autoregressive PDE Surrogates
Enrique Hernández Noguera, Md Meftahul Ferdaus, Elias Ioup +2
Neural operators for time-dependent PDEs face a structural tension: spectral architectures (FNO and descendants) inherit exponential rollout-error growth from their one-step Lipsch…
Accelerating HEC-RAS: A Recurrent Neural Operator for Rapid River Forecasting
Edward Holmberg, Pujan Pokhrel, Maximilian Zoch +9
Physics-based solvers like HEC-RAS provide high-fidelity river forecasts but are too computationally intensive for on-the-fly decision-making during flood events. The central chall…
Physics-Informed Neural Network Surrogate Models for River Stage Prediction
Maximilian Zoch, Edward Holmberg, Pujan Pokhrel +8
This work investigates the feasibility of using Physics-Informed Neural Networks (PINNs) as surrogate models for river stage prediction, aiming to reduce computational cost while m…
STROOBnet Optimization via GPU-Accelerated Proximal Recurrence Strategies
Ted Edward Holmberg, Mahdi Abdelguerfi, Elias Ioup
Spatiotemporal networks' observational capabilities are crucial for accurate data gathering and informed decisions across multiple sectors. This study focuses on the Spatiotemporal…