Articles | Volume 13, issue 4
https://doi.org/10.5194/esurf-13-563-2025
https://doi.org/10.5194/esurf-13-563-2025
Short communication
 | 
18 Jul 2025
Short communication |  | 18 Jul 2025

Short communication: Learning how landscapes evolve with neural operators

Gareth G. Roberts

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-307', Christoph Glotzbach, 13 Mar 2025
  • RC2: 'Comment on egusphere-2025-307', Anonymous Referee #2, 16 Mar 2025
  • AC1: 'Comment on egusphere-2025-307', Gareth G. Roberts, 04 Apr 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Gareth G. Roberts on behalf of the Authors (04 Apr 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to minor revisions (review by editor) (05 May 2025) by Simon Mudd
AR by Gareth G. Roberts on behalf of the Authors (09 May 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (09 May 2025) by Simon Mudd
ED: Publish as is (11 May 2025) by Wolfgang Schwanghart (Editor)
AR by Gareth G. Roberts on behalf of the Authors (12 May 2025)
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Short summary
The use of new artificial intelligence (AI) techniques to learn how landscapes evolve is demonstrated. A few “snapshots” of an eroding landscape at different stages of its history provide enough information for AI to ascertain rules governing its evolution. Once the rules are known, predicting landscape evolution is extremely rapid and efficient, providing new tools to understand landscape change.
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