Complete flow characterization from snapshot PIV, fast probes and physics-informed neural networks

The use of physics-informed neural networks (PINNs), based on the incorporation of governing laws to constrain the training of machine-learning algorithms, has widened the possibilities for artificial intelligence to model and regularize experimental data. PINNs have been recently shown to improve the accuracy of time-resolved measurements, but their capabilities are remarkably reduced when time resolution is not available. In this work, we exploit PINNs to enhance velocity measurements from non-time-resolved field measurements, such as those from snapshot Particle Image Velocimetry (PIV). We use PINNs as a regularizer of time-resolved estimated fields from simultaneous measurements with fast pointwise probes and non-time-resolved PIV. We make use of a multilayer perceptron architecture to set a correspondence between probe data and the temporal coefficients of the Proper Orthogonal Decomposition of the PIV velocity profiles. The estimated time-resolved fields are then fed to the PINNs to enhance data accuracy and additionally extract derived quantities such as the pressure field.