{"ID":2832807,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.04434","arxiv_id":"2512.04434","title":"Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks","abstract":"Fast, geometry-generalizing surrogates for unsteady flow remain challenging. We present a time-dependent, geometry-aware Deep Operator Network that predicts velocity fields for moderate-Re flows around parametric and non-parametric shapes. The model encodes geometry via a signed distance field (SDF) trunk and flow history via a CNN branch, trained on 841 high-fidelity simulations. On held-out shapes, it attains $\\sim 5\\%$ relative L2 single-step error and up to 1000X speedups over CFD. We provide physics-centric rollout diagnostics, including phase error at probes and divergence norms, to quantify long-horizon fidelity. These reveal accurate near-term transients but error accumulation in fine-scale wakes, most pronounced for sharp-cornered geometries. We analyze failure modes and outline practical mitigations. Code, splits, and scripts are openly released at: https://github.com/baskargroup/TimeDependent-DeepONet to support reproducibility and benchmarking.","short_abstract":"Fast, geometry-generalizing surrogates for unsteady flow remain challenging. We present a time-dependent, geometry-aware Deep Operator Network that predicts velocity fields for moderate-Re flows around parametric and non-parametric shapes. The model encodes geometry via a signed distance field (SDF) trunk and flow hist...","url_abs":"https://arxiv.org/abs/2512.04434","url_pdf":"https://arxiv.org/pdf/2512.04434v1","authors":"[\"Ali Rabeh\",\"Suresh Murugaiyan\",\"Adarsh Krishnamurthy\",\"Baskar Ganapathysubramanian\"]","published":"2025-12-04T04:00:22Z","proceeding":"physics.flu-dyn","tasks":"[\"physics.flu-dyn\",\"cs.LG\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false,"code_links":[{"ID":606277,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2832807,"paper_url":"https://arxiv.org/abs/2512.04434","paper_title":"Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks","repo_url":"https://github.com/baskargroup/TimeDependent-DeepONet","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}