<ul><li>KITINet is a novel neural network architecture inspired by kinetics theory and PDE simulation approaches.</li><li>It introduces a residual module that models feature updates as the stochastic evolution of a particle system, simulated via a discretized solver for the Boltzmann transport equation.</li><li>This formulation enables adaptive feature refinement through physics-informed interactions and induces network parameter condensation during training.</li><li>Experimental results across scientific computation, image classification, and text classification demonstrate consistent improvements over traditional network baselines with minimal increase in computational complexity.</li></ul>

KITINet: Kinetics Theory Inspired Network Architectures with PDE Simulation Approaches

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