Safe-by-Design Learning via Energy-based Neural Networks
This paper integrates modern Hopfield networks with a port-Hamiltonian neural ODE, enabling by design the construction of barrier functions yielding explicit admissible-input sets and quantitative robustness radii that are more robust to external solicitations than comparable existing approaches.
Simone Betteti, Morteza Lahijanian, Luca Laurenti
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