{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:44:56Z","timestamp":1782841496150,"version":"3.54.5"},"reference-count":0,"publisher":"ECMS","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,23]]},"abstract":"<jats:p>Data-driven surrogates are increasingly used to accelerate simulations of unsteady flows with moving boundaries, yet their suitability for long-horizon prediction remains an open question. This work compares two representative learning approaches: motion-conditioned latent dynamical models based on recurrent neural networks and motion-conditioned operator-learning approaches based on Fourier Neural Operators enhanced with autoregressive consistency and physics-based temporal regularization (AR-FNO). Using a heaving square cylinder as a canonical test case, we assess short-horizon reconstruction accuracy, error growth during autoregressive rollout, and spatial flow-field behaviour. A floating wind-turbine configuration is included as a supplementary case to assess performance in a more complex flow environment. Both models achieve comparable short-horizon accuracy. During extended autoregressive rollout, the ConvLSTM exhibits progressive phase drift and amplitude distortion, whereas the AR-FNO maintains improved phase consistency and lower cumulative RMSE. In the square-cylinder case, the AR-FNO reduces the long-horizon cumulative error growth by approximately 15 - 20 % relative to the latent-dynamical surrogate, thereby demonstrating improved performance.<\/jats:p>","DOI":"10.7148\/2026-0411","type":"proceedings-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:55:14Z","timestamp":1782838514000},"page":"411-418","source":"Crossref","is-referenced-by-count":0,"title":["Simulating moving-boundary flows with neural surrogates: latent dynamics vs operator learning"],"prefix":"10.7148","author":[{"given":"Haris Hameed","family":"Mian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fadi","family":"Al Machot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicoletta","family":"Franchina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Otman","family":"Kouaissah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Saeed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"uhammad Salman","family":"Siddiqui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4144","published-online":{"date-parts":[[2026,6,23]]},"event":{"name":"40th ECMS International Conference on Modelling and Simulation"},"container-title":["ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina"],"original-title":[],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:55:16Z","timestamp":1782838516000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.scs-europe.net\/dlib\/2026\/ecms2026acceptedpapers\/0411_simai_ecms2026_0077.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":0,"URL":"https:\/\/doi.org\/10.7148\/2026-0411","relation":{},"subject":[],"published":{"date-parts":[[2026,6,23]]}}}