Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915653299994624 |
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| author | Rabeh, Ali Murugaiyan, Suresh Krishnamurthy, Adarsh Ganapathysubramanian, Baskar |
| author_facet | Rabeh, Ali Murugaiyan, Suresh Krishnamurthy, Adarsh Ganapathysubramanian, Baskar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04434 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks Rabeh, Ali Murugaiyan, Suresh Krishnamurthy, Adarsh Ganapathysubramanian, Baskar Fluid Dynamics Machine Learning 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. |
| title | Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks |
| topic | Fluid Dynamics Machine Learning |
| url | https://arxiv.org/abs/2512.04434 |