New SGS model for turbulence uses mutual information maximization tied to inter-scale equilibrium to estimate parameters without empirical prescription, matching prior values and performing comparably in box and channel flow tests.
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A video-to-PDE pipeline extracts the model u_t + v(t)·∇u = 9.005|∇u|^2 + 0.666Δu from grayscale ink-plume footage, outperforming advection-diffusion baselines on held-out frames and reducing to linear form via Cole-Hopf transformation.
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.
citing papers explorer
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Information-Preserving SGS model based on the local inter-scale equilibrium hypothesis
New SGS model for turbulence uses mutual information maximization tied to inter-scale equilibrium to estimate parameters without empirical prescription, matching prior values and performing comparably in box and channel flow tests.
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From Video-to-PDE: Data-Driven Discovery of Nonlinear Dye Plume Dynamics
A video-to-PDE pipeline extracts the model u_t + v(t)·∇u = 9.005|∇u|^2 + 0.666Δu from grayscale ink-plume footage, outperforming advection-diffusion baselines on held-out frames and reducing to linear form via Cole-Hopf transformation.
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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.