A bidirectional GRU trained on synthetic data from a 1D hydraulic RC circuit model can recover power-law viscosity parameters (η0, n, λ) from microfluidic pressure and flow signals, in simulation only.
Simultaneous measurement of rheological properties in a microfluidic rheometer
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Methodology for Online Estimation of Rheological Parameters in Polymer Melts Using Deep Learning and Microfluidics
A bidirectional GRU trained on synthetic data from a 1D hydraulic RC circuit model can recover power-law viscosity parameters (η0, n, λ) from microfluidic pressure and flow signals, in simulation only.