Physics-informed VAEs with Hamiltonian-based losses generate trajectories that match training distributions and satisfy optimal-control equations from as few as 200 to 500 samples.
15 Sample outputs of the Split-VAE for 200 training samples for饾渾= 5
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Case Studies of Generative Machine Learning Models for Dynamical Systems
Physics-informed VAEs with Hamiltonian-based losses generate trajectories that match training distributions and satisfy optimal-control equations from as few as 200 to 500 samples.