NSIPF represents density via empirical particle measures and the field via a CNN trained on synthetic data, preserving mass and nonnegativity while simulating 3D multi-bump chemotaxis dynamics faster than finite difference or standard SIPF methods.
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An Efficient Particle-Field Algorithm with Neural Interpolation based on a Parabolic-Hyperbolic Chemotaxis System in 3D
NSIPF represents density via empirical particle measures and the field via a CNN trained on synthetic data, preserving mass and nonnegativity while simulating 3D multi-bump chemotaxis dynamics faster than finite difference or standard SIPF methods.