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This paper introduces Latent Lie-Poisson Neural Networks (LLPNNs), a structure-preserving framework for learning Lie-Poisson dynamics directly from observable data, using geometric methods and Magnus-based Lie-group updates. It demonstrates strong accuracy and robustness on rigid body, underwater vehicle, and optimal control examples.
This paper introduces SHAPE, a structured adaptive port-Hamiltonian optimizer for fixed-budget nonconvex optimization that uses event-triggered mechanisms to balance descent, exploration, and budget allocation.