A backpropagation-free training approach for Hamiltonian neural networks via data-driven parameter sampling that claims over 100x CPU speedup and four orders of magnitude better accuracy on chaotic systems like Hénon-Heiles compared to gradient-based methods.
Visual interaction networks: Learning a physics simulator from video
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Training Hamiltonian neural networks without backpropagation
A backpropagation-free training approach for Hamiltonian neural networks via data-driven parameter sampling that claims over 100x CPU speedup and four orders of magnitude better accuracy on chaotic systems like Hénon-Heiles compared to gradient-based methods.