Physics-constrained DeepONet for Surrogate CFD models: a curved backward-facing step case

• URL: http://arxiv.org/abs/2503.11196v1
• Date: Fri, 14 Mar 2025 08:43:36 GMT
• Title: Physics-constrained DeepONet for Surrogate CFD models: a curved backward-facing step case
• Authors: Anas Jnini, Harshinee Goordoyal, Sujal Dave, Flavio Vella, Katharine H. Fraser, Artem Korobenko,
• Abstract summary: The PC-DeepONet architecture incorporates fundamental physics knowledge into the data-driven DeepONet model.<n>Both models attain convergence with a small dataset of 50 samples and require only 50 iterations for convergence.
• Score: 0.9565934024763958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Physics-Constrained DeepONet (PC-DeepONet), an architecture that incorporates fundamental physics knowledge into the data-driven DeepONet model, is presented in this study. This methodology is exemplified through surrogate modeling of fluid dynamics over a curved backward-facing step, a benchmark problem in computational fluid dynamics. The model was trained on computational fluid dynamics data generated for a range of parameterized geometries. The PC-DeepONet was able to learn the mapping from the parameters describing the geometry to the velocity and pressure fields. While the DeepONet is solely data-driven, the PC-DeepONet imposes the divergence constraint from the continuity equation onto the network. The PC-DeepONet demonstrates higher accuracy than the data-driven baseline, especially when trained on sparse data. Both models attain convergence with a small dataset of 50 samples and require only 50 iterations for convergence, highlighting the efficiency of neural operators in learning the dynamics governed by partial differential equations.

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