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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A physics-informed neural network merges sparse LBM data with Navier-Stokes equations to predict unsteady flows in fractal-rough microchannels at 150-200 times lower data cost.
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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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Amalgamation of Physics-Informed Neural Network and LBM for the Prediction of Unsteady Fluid Flows in Fractal-Rough Microchannels
A physics-informed neural network merges sparse LBM data with Navier-Stokes equations to predict unsteady flows in fractal-rough microchannels at 150-200 times lower data cost.