K-PINN integrates Lattice-Boltzmann kinetics into a U-Net architecture to model droplet wettability on complex surfaces with L2 errors of 0.021-0.026, R² near 0.999, and mass conservation within 1.5% while enabling real-time inference.
(19) Rabinovich, M.; Huerta, R.; Laurent, G., Transient dynamics for neural processing
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Non-steady-state chemical charge transport dynamics integrated into reservoir computing enable waveform recognition, voice identification, and chaos prediction, with performance governed by frequency alignment that functions as a chemically-tuned band-pass filter.
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Lattice-Boltzmann-Driven Physics-Informed Neural Networks for Droplet Wettability on Rough Surfaces
K-PINN integrates Lattice-Boltzmann kinetics into a U-Net architecture to model droplet wettability on complex surfaces with L2 errors of 0.021-0.026, R² near 0.999, and mass conservation within 1.5% while enabling real-time inference.
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Exploring Non-Steady-State Charge Transport Dynamics in Information Processing: Insights from Reservoir Computing
Non-steady-state chemical charge transport dynamics integrated into reservoir computing enable waveform recognition, voice identification, and chaos prediction, with performance governed by frequency alignment that functions as a chemically-tuned band-pass filter.