Deriving a neural cellular automaton from locality, symmetry, and stability postulates produces 100% accurate addition generalization from 16-digit to 1-million-digit inputs.
Universality and Complexity in Cellular Automata
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Presents UFDSM based on cellular automata with a conservation nonstandard finite difference method that preserves positivity and fixed points, showing small discrepancies versus HEC-RAS in validation.
Neural network learning opacity stems from three dynamical complexity properties in training, rendering some sources of opacity irreducible.
citing papers explorer
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A Novel Urban Flood Dynamical System Model and a Corresponding Nonstandard Finite Difference Method
Presents UFDSM based on cellular automata with a conservation nonstandard finite difference method that preserves positivity and fixed points, showing small discrepancies versus HEC-RAS in validation.