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DiffSWE2d: a differentiable Shallow Water Equations solver for end-to-end flood and tsunami modelling — arXiv:2609.11426

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.11426
License (as archived): CC0-1.0
Archived: 2026-10-16T07:00:25.897Z
SHA-256 of the archived text: 0cefa01b9cc9c29b8f6f29aed593bed78816a324133ba123e832e04fc9aa2af8
Stable URL of this page: https://metraipolis.com/documento/dc_970c2a85bbb6

How to cite

DiffSWE2d: a differentiable Shallow Water Equations solver for end-to-end flood and tsunami modelling — arXiv:2609.11426 — arXiv (en), https://arxiv.org/abs/2609.11426 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_970c2a85bbb6 (Metraipolis archive dc_970c2a85bbb6, sha256 0cefa01b9cc9c29b8f6f29aed593bed78816a324133ba123e832e04fc9aa2af8)

BibTeX

@misc{dc970c2a85bbb6,
  title = {DiffSWE2d: a differentiable Shallow Water Equations solver for end-to-end flood and tsunami modelling — arXiv:2609.11426},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.11426},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_970c2a85bbb6; sha256 0cefa01b9cc9c29b8f6f29aed593bed78816a324133ba123e832e04fc9aa2af8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.11426. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Solving inverse and optimisation problems with traditional shallow water equations (SWE) solvers can be computationally expensive, particularly when gradients with respect to model inputs or parameters must be estimated through repeated forward simulations. In this paper, we introduce DiffSWE2d, an open-source differentiable shallow water equations solver for end-to-end flood and tsunami modelling implemented in PyTorch. By leveraging automatic differentiation, DiffSWE2d represents the time-marching physics as a differentiable computational graph, enabling gradients to be propagated directly through the numerical solver. We validate the solver against two established benchmark cases and demonstrate its application to tsunami waveform inversion, showing its ability to infer model inputs through gradient-based optimisation. DiffSWE2d provides a flexible framework for integrating physics-based hydrodynamic modelling with modern optimisation and machine learning methods. The source code and reproducible examples are publicly available at: this https URL

Tema degli esploratori: terra, clima ed energia (retro-catalogo arXiv: annunci del 2026-09-11 in physics.geo-ph).
Categorie arXiv: Fluid Dynamics (physics.flu-dyn) ; Geophysics (physics.geo-ph).
Pagina del lavoro: https://arxiv.org/abs/2609.11426 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T06:39:40.014Z dalla pagina https://arxiv.org/catchup/physics.geo-ph/2026-09-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).