Show HN: Training a Hamiltonian Neural Netwrork from Scratch in PyTorch (github.com)

A new project on GitHub showcases the implementation and training of Hamiltonian Neural Networks (HNNs) using PyTorch, specifically modeling the dynamics of a nonlinear pendulum. HNNs differ from traditional neural networks by learning the Hamiltonian of a system, which enables them to adhere to core physical principles such as energy conservation. This approach is significant for the AI/ML community, as it opens up new avenues for simulating complex physical systems, ensuring that the learned representations respect fundamental conservation laws. The repository includes essential components such as an HNN architecture capable of outputting a learned Hamiltonian, a training script that optimizes predictions based on synthetic pendulum data, and tools for simulating trajectories in phase space. Key technical innovations include the use of PyTorch's autograd for computing time derivatives and efficient batch processing for simulating pendulum dynamics. The results demonstrate the HNN's ability to capture periodic orbits and maintain energy conservation over extended periods, outperforming conventional recurrent neural networks (RNNs) and multilayer perceptrons (MLPs) in this context. This project promises exciting implications for the future of physics-informed machine learning.