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pith:34GSLIUF

pith:2026:34GSLIUFFAO4V7MGIVX2VHHKNX

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Toward AI-Driven Digital Twins for Metropolitan Floods: A Conditional Latent Dynamics Network Surrogate of the Shallow Water Equations

Eugene Yan, Jeremy Feinstein, Omar Sallam, Peng Chen, Phillip Si, Yuan Qiu, Ziang He

CLDNet uses a rainfall-driven latent neural ODE and terrain-conditioned decoder to surrogate the shallow water equations, producing 96-hour metropolitan flood forecasts in 29 seconds.

arxiv:2605.13761 v1 · 2026-05-13 · cs.LG · cs.CE

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Claims

C1strongest claim

CLDNet produces a full 96-hour basin-wide forecast in ~29 seconds -- a ~115× speedup -- while reaching a critical success index of ≈86% at the 0.5 m inundation threshold and roughly halving the relative RMSE of an unconditional baseline on the Des Plaines case study.

C2weakest assumption

The latent neural ODE dynamics, trained on simulator output, continue to generalize accurately to unseen rainfall patterns and to the full range of terrain features without accumulating errors over the 96-hour horizon.

C3one line summary

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.

References

41 extracted · 41 resolved · 2 Pith anchors

[1] J. Rentschler, M. Salhab, B. A. Jafino, Flood exposure and poverty in 188 countries, Nature Commu- nications 13 (1) (2022) 3527.doi:10.1038/s41467-022-30727-4 2022 · doi:10.1038/s41467-022-30727-4

[2] B. Tellman, J. A. Sullivan, C. Kuhn, A. J. Kettner, C. S. Doyle, G. R. Brakenridge, T. A. Erickson, D. A. Slayback, Satellite imaging reveals increased proportion of population exposed to floods, Natu 2021 · doi:10.1038/s41586-021-03695-w

[3] P. D. Bates, M. S. Horritt, T. J. Fewtrell, A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling, Journal of Hydrology 387 (1–2) (2010) 2010 · doi:10.1016/j.jhydrol.2010.03.027

[4] X. Xia, Q. Liang, X. Ming, J. Hou, An efficient and stable hydrodynamic model with novel source term discretization schemes for overland flow and flood simulations, Water resources research 53 (5) (20 2017 · doi:10.1002/2016wr020055

[5] X. Xia, Q. Liang, A new efficient implicit scheme for discretising the stiff friction terms in the shallow water equations, Advances in water resources 117 (2018) 87–97.doi:10.1016/j.advwatres.2018.05 2018 · doi:10.1016/j.advwatres.2018.05

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Receipt and verification

First computed	2026-05-18T02:44:16.171734Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

df0d25a285281dcafd86456faa9cea6de740cad95510151cd524b994da35d04d

Aliases

arxiv: 2605.13761 · arxiv_version: 2605.13761v1 · doi: 10.48550/arxiv.2605.13761 · pith_short_12: 34GSLIUFFAO4 · pith_short_16: 34GSLIUFFAO4V7MG · pith_short_8: 34GSLIUF

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/34GSLIUFFAO4V7MGIVX2VHHKNX \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: df0d25a285281dcafd86456faa9cea6de740cad95510151cd524b994da35d04d

Canonical record JSON

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    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-13T16:41:14Z",
    "title_canon_sha256": "398dcb4548ca70cf8574cbea280ce802c16205c11feba37724d6215672d15ca4"
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