# Flow based features and validation metric for machine learning reconstruction of PIV data

arXiv: https://arxiv.org/abs/2105.13429 · HTML: https://mlsanity.com/paper/2105.13429/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 68 references that are in the map.
- First posted on arXiv 27 May 2021.

## Builds on (most cited first, 2 of 2)

- [Using machine learning to construct velocity fields from OH-PLIF images](https://mlsanity.com/paper/1909.13669/) (2019)
- [Particle reconstruction of volumetric particle image velocimetry with strategy of machine learning](https://mlsanity.com/paper/1909.07815/) (2019)

## Built on by (most cited first, 1 of 1)

- [Physics-informed deep-learning applications to experimental fluid mechanics](https://mlsanity.com/paper/2203.15402/) (2022)

## Abstract (arXiv)

Reconstruction of flow field from real sparse data by a physics-oriented approach is a current challenge for fluid scientists in the AI community. The problem includes feature recognition and implementation of AI algorithms that link data to a physical feature space in order to produce reconstructed data. The present article applies machine learning approach to study contribution of different flow-based features with practical fluid mechanics applications for reconstruction of the missing data of turbomachinery PIV measurements. Support vector regression (SVR) and multi-layer perceptron (MLP) are selected as two robust regressors capable of modelling non-linear fluid flow phenomena. The proposed flow-based features are optimally scaled and filtered to extract the best configuration. In addition to conventional data-based validation of the regressors, a metric is proposed that reflects mass conservation law as an important requirement for a physical flow reproduction. For a velocity field including 25% of clustered missing data, the reconstruction accuracy achieved by SVR in terms of R2-score is as high as 0.993 for the in-plane velocity vectors in comparison with that obtained by MLP which is up to 0.981. In terms of mass conservation metric, the SVR model by R2-score up to 0.96 is considerably more accurate than the MLP estimator. For extremely sparse data with a gappiness of 75%, vector and contour plots from SVR and MLP were consistent with those of the original field.