Graph neural operator for wind farm wake flow

Jens Peter Schøler, Frederik Peder Weilmann Rasmussen, Julian Quick, and Pierre-Elouan Réthoré

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Month	HTML	PDF	XML	Total
Dec 2025	189	119	12	320
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Mar 2026	75	74	4	153
Apr 2026	91	91	3	185
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Jul 2026	129	57	9	195
Aug 2026	77	32	1	110

Cumulative views and downloads (calculated since 04 Dec 2025)

Month	HTML	PDF	XML	Total
Dec 2025	189	119	12	320
Jan 2026	146	93	7	246
Feb 2026	92	71	6	169
Mar 2026	75	74	4	153
Apr 2026	91	91	3	185
May 2026	69	82	4	155
Jun 2026	18	48	2	68
Jul 2026	129	57	9	195
Aug 2026	77	32	1	110

HTML	PDF	XML	Total	BibTeX	EndNote
173	51	8	232	10	8

HTML	PDF	XML	Total	BibTeX	EndNote
713	616	40	1,369	41	43

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Saved (final revised paper)

Latest update: 21 Aug 2026

Short summary

We propose a machine learning model that represents wind turbines as nodes in a graph, learning how they interact to predict the wind speed at any location on a farm. Trained on thousands of simulated farms, it captures complex wake interactions across varying layouts and sizes at low computational cost, supporting faster neighboring farm wake assessment.