CLDNet is a conditional latent dynamics network surrogate for the shallow water equations that delivers 115x faster 96-hour flood forecasts on irregular metropolitan basins while maintaining usable accuracy against gauge data.
Bates, Matthew S
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
An EnsCGP coarse surrogate plus U-Net-ASPP corrector emulates LISFLOOD-FP flood depths on a 256x256 grid around one Chicago gauge, achieving R² ≈ 0.99 and MAE < 0.01 m on held-out events while matching the gauge depth at that single pixel.
A tool combining full hydrodynamic modeling with a bespoke evolutionary algorithm to optimize blue-green infrastructure for reducing urban flood vulnerability at property scale.
citing papers explorer
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Toward AI-Driven Digital Twins for Metropolitan Floods: A Conditional Latent Dynamics Network Surrogate of the Shallow Water Equations
CLDNet is a conditional latent dynamics network surrogate for the shallow water equations that delivers 115x faster 96-hour flood forecasts on irregular metropolitan basins while maintaining usable accuracy against gauge data.
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Observation-Guided Neural Surrogate Learning for Scientific Simulation Emulation: A Single-Gauge Flood-Inundation Proof of Concept
An EnsCGP coarse surrogate plus U-Net-ASPP corrector emulates LISFLOOD-FP flood depths on a 256x256 grid around one Chicago gauge, achieving R² ≈ 0.99 and MAE < 0.01 m on held-out events while matching the gauge depth at that single pixel.
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Optimising Urban Flood Resilience
A tool combining full hydrodynamic modeling with a bespoke evolutionary algorithm to optimize blue-green infrastructure for reducing urban flood vulnerability at property scale.