{"slug": "neural-operators-for-immersed-boundary-soft-swimmers-locomotion", "title": "Neural Operators for Immersed-Boundary Soft Swimmers Locomotion", "summary": "Researchers developed neural-operator surrogates that predict hydrodynamic fields for planar and volumetric eel swimmers, achieving a full-domain global relative L2 error of 3.51% on five held-out high-Reynolds-number trajectories for the planar model. The volumetric model, using three target-specific models, achieved errors of 3.44% for velocity, 5.58% for vorticity, and 19.2% for pressure on five held-out within-range trajectories. The findings, reported in arXiv:2608.07722v1, demonstrate the feasibility of field-resolved neural surrogates for moving-boundary swimmer flows while highlighting pressure accuracy and physical consistency as areas for improvement.", "body_md": "arXiv:2608.07722v1 Announce Type: new\nAbstract: High-fidelity immersed-boundary simulation resolves the coupled motion of a deforming swimmer and its surrounding flow, but the resulting cost limits repeated evaluations for engineering design, parameter studies, and control. We develop neural-operator surrogates for temporal prediction of the hydrodynamic fields generated by planar and volumetric eel swimmers. The surrogates are trained on regular-grid fields exported from adaptive fluid--structure simulations and are conditioned on swimmer geometry and Reynolds number. The planar model jointly predicts two velocity components, scalar vorticity, and pressure. On five held-out high-Reynolds-number trajectories, its full-domain global relative L^2 error is 3.51 %. The volumetric formulation uses three target-specific models with a common multichannel input: one model predicts three-dimensional velocity, one predicts vorticity, and one predicts pressure. Their full-domain global relative L^2 errors on five held-out within-range trajectories are 3.44 %, 5.58 %, and 19.2 %. Together, the results demonstrate the feasibility of field-resolved neural surrogates for moving-boundary swimmer flows while identifying pressure accuracy and physical consistency as priorities for further development.", "url": "https://wpnews.pro/news/neural-operators-for-immersed-boundary-soft-swimmers-locomotion", "canonical_source": "https://arxiv.org/abs/2608.07722", "published_at": "2026-08-11 04:00:00+00:00", "updated_at": "2026-08-11 04:11:32.092976+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/neural-operators-for-immersed-boundary-soft-swimmers-locomotion", "markdown": "https://wpnews.pro/news/neural-operators-for-immersed-boundary-soft-swimmers-locomotion.md", "text": "https://wpnews.pro/news/neural-operators-for-immersed-boundary-soft-swimmers-locomotion.txt", "jsonld": "https://wpnews.pro/news/neural-operators-for-immersed-boundary-soft-swimmers-locomotion.jsonld"}}