Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks
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.
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Ali Rabeh
claimed_verified
2025-12-09T09:21:10.610Z
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/avatars/e48205e66a7ab81328eabe6ece93c7ff.svg
Ali Rabeh
arabeh
user
arabeh
6937deda19d912300c34a4d1
Suresh Murugaiyan
6937deda19d912300c34a4d2
Adarsh Krishnamurthy
admin_assigned
2025-12-10T13:23:36.769Z
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/avatars/d4b0132d9d8cd7f02fc61f4bb3644204.svg
Adarsh Krishnamurthy
k-adarsh
user
k-adarsh
6937deda19d912300c34a4d3
Baskar Ganapathysubramanian
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2025-12-22T11:01:12.382Z
6668e0db90a13300e88137f2
/avatars/dca6a664631924e5ff0e8393bb2772af.svg
Baskar Group
BGLab
user
BGLab
Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks
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.
A geometry-aware Deep Operator Network encodes shape and flow history to predict velocity fields for unsteady flows, achieving significant speedup over CFD simulations.
Deep Operator Network signed distance field CNN high-fidelity simulations relative L2 error phase error divergence norms failure modes mitigations