  Accurately determining fluid viscosity is crucial for various industrial and
scientific applications. Traditional methods of viscosity measurement, though
reliable, often require manual intervention and cannot easily adapt to
real-time monitoring. With advancements in machine learning and computer
vision, this work explores the feasibility of predicting fluid viscosity by
analyzing fluid oscillations captured in video data. The pipeline employs a 3D
convolutional autoencoder pretrained in a self-supervised manner to extract and
learn features from semantic segmentation masks of oscillating fluids. Then,
the latent representations of the input data, produced from the pretrained
autoencoder, is processed with a distinct inference head to infer either the
fluid category (classification) or the fluid viscosity (regression) in a
time-resolved manner. When the latent representations generated by the
pretrained autoencoder are used for classification, the system achieves a 97.1%
accuracy across a total of 4,140 test datapoints. Similarly, for regression
tasks, employing an additional fully-connected network as a regression head
allows the pipeline to achieve a mean absolute error of 0.258 over 4,416 test
datapoints. This study represents an innovative contribution to both fluid
characterization and the evolving landscape of Artificial Intelligence,
demonstrating the potential of deep learning in achieving near real-time
viscosity estimation and addressing practical challenges in fluid dynamics
through the analysis of video data capturing oscillating fluid dynamics.
