<ul><li>This paper introduces the approach of learning verifiable contraction metrics for discrete-time nonlinear dynamical systems.</li><li>The paper addresses the challenge of identifying contraction metrics for complex nonlinear systems using neural networks.</li><li>A new sufficient condition is established for formal neural contraction metrics, assuming continuity of the dynamics.</li><li>The paper validates the approach through successful synthesis and verification of neural contraction metrics for various nonlinear examples.</li></ul>

Neural Contraction Metrics with Formal Guarantees for Discrete-Time Nonlinear Dynamical Systems

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