GKAN-ODE, a KAN-based symbolic regression model with a dedicated evaluation benchmark, is claimed to recover exact governing equations of graph dynamical systems and beat baselines by up to two orders of magnitude in out-of-distribution trajectory error.
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Discovering Generalizable Governing Equations for Graph Dynamical Systems with Interpretable Neural Networks
GKAN-ODE, a KAN-based symbolic regression model with a dedicated evaluation benchmark, is claimed to recover exact governing equations of graph dynamical systems and beat baselines by up to two orders of magnitude in out-of-distribution trajectory error.