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Found 4 papers, 1 papers with code
Can physical information aid the generalization ability of Neural Networks for hydraulic modeling?
Application of Neural Networks to river hydraulics is fledgling, despite the field suffering from data scarcity, a challenge for machine learning techniques.
Benchmarking YOLOv5 and YOLOv7 models with DeepSORT for droplet tracking applications
This work is a benchmark study for the YOLOv5 and YOLOv7 networks with DeepSORT in terms of the training time and inference time for a custom dataset of microfluidic droplets.
DropTrack -- automatic droplet tracking using deep learning for microfluidic applications
One fundamental analysis frequently desired in microfluidic experiments is counting and tracking the droplets.
Lattice Boltzmann Multicomponent Model for Direct-Writing Printing
We introduce a mesoscale approach for the simulation of multicomponent flows to model the direct-writing printing process, along with the early stage of ink deposition.
Fluid Dynamics Computational Physics
