  Drawing inspiration from the lateral lines of fish, the inference of flow
characteristics via surface-based data has drawn considerable attention. The
current approaches often rely on analytical methods tailored exclusively for
potential flows or utilize black-box machine learning algorithms to estimate a
specific set of flow parameters. In contrast to a black box machine learning
approach, we demonstrate that it is possible to identify certain modes of fluid
flow and then reconstruct the entire flow field from these modes. We use
Dynamic Mode Decomposition (DMD) to parametrize complex, dynamic features
across the entire flow field. We then leverage deep neural networks to infer
the DMD modes of the pressure and velocity fields within a large, unsteady flow
domain, employing solely a time series of pressure measurements collected on
the surface of an immersed obstacle. Our methodology is successfully
demonstrated to diverse fluid-structure interaction scenarios, including cases
with both free oscillations in the wake of a cylinder and forced oscillations
of tandem cylinders, demonstrating its versatility and robustness.
