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Found 5 papers, 2 papers with code
Evidence of Scaling Regimes in the Hopfield Dynamics of Whole Brain Model
It is shown that a Hopfield recurrent neural network, informed by experimentally derived brain topology, recovers the scaling picture recently introduced by Deco et al., according to which the process of information transfer within the human brain shows spatially correlated patterns qualitatively similar to those displayed by turbulent flows.
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.
Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers
The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow.
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
