Deep reinforcement learning-based digital twin for droplet microfluidics control
Nafisat Gyimah,
Ott Scheler,
Toomas Rang
et al.
Abstract:This study applied deep reinforcement learning (DRL) with the Proximal Policy Optimization (PPO) algorithm within a two-dimensional computational fluid dynamics (CFD) model to achieve closed-loop control in microfluidics. The objective was to achieve the desired droplet size with minimal variability in a microfluidic capillary flow-focusing device. An artificial neural network was utilized to map sensing signals (flow pressure and droplet size) to control actions (continuous phase inlet pressure). To validate … Show more
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