mlsanityData to 14 Oct 2026

Physics-informed neural networks modeling for systems with moving immersed boundaries: application to an unsteady flow past a plunging foil

arXiv 2306.13395 ↗First posted 23 Jun 2023physics.flu-dyn cs.LGcited by 4

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Abstract (arXiv)

Recently, physics informed neural networks (PINNs) have been explored extensively for solving various forward and inverse problems and facilitating querying applications in fluid mechanics applications. However, work on PINNs for unsteady flows past moving bodies, such as flapping wings is scarce. Earlier studies mostly relied on transferring to a body attached frame of reference which is restrictive towards handling multiple moving bodies or deforming structures. Hence, in the present work, an immersed boundary aware framework has been explored for developing surrogate models for unsteady flows past moving bodies. Specifically, simultaneous pressure recovery and velocity reconstruction from Immersed boundary method (IBM) simulation data has been investigated. While, efficacy of velocity reconstruction has been tested against the fine resolution IBM data, as a step further, the pressure recovered was compared with that of an arbitrary Lagrange Eulerian (ALE) based solver. Under this framework, two PINN variants, (i) a moving-boundary-enabled standard Navier-Stokes based PINN (MB-PINN), and, (ii) a moving-boundary-enabled IBM based PINN (MB-IBM-PINN) have been formulated. A fluid-solid partitioning of the physics losses in MB-IBM-PINN has been allowed, in order to investigate the effects of solid body points while training. This enables MB-IBM-PINN to match with the performance of MB-PINN under certain loss weighting conditions. MB-PINN is found to be superior to MB-IBM-PINN when it a priori knowledge of the solid body position and velocity are available. To improve the data efficiency of MB-PINN, a physics based data sampling technique has also been investigated. It is observed that a suitable combination of physics constraint relaxation and physics based sampling can achieve a model performance comparable to the case of using all the data points, under a fixed training budget.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 26 of its 73 references that are in the map. First posted on arXiv 23 Jun 2023.

Builds on

26 of 73 references.

References · 26

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14 more
  1. 2023Machine learning with data assimilation and uncertainty quantification for dynamical systems: a reviewcited by 75
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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networkscited by 12
  2. 2025Sequential learning based PINNs to overcome temporal domain complexities in unsteady flow past flapping wingscited by 2
  3. 2024Understanding the training of PINNs for unsteady flow past a plunging foil through the lens of input subdomain level loss function gradientscited by 0
  4. 2025Learning Fluid-Structure Interaction with Physics-Informed Machine Learning and Immersed Boundary Methodscited by 0