Neural Hamilton

This repository contains the official implementation of the paper "Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?"

Overview

Neural Hamilton reformulates Hamilton's equations as an operator learning problem, exploring whether artificial intelligence can grasp the principles of Hamiltonian mechanics without explicitly solving differential equations. The project introduces new neural network architectures specifically designed for operator learning in Hamiltonian systems.

Key features: - Novel algorithm for generating physically plausible potential functions using Gaussian Random Fields and cubic B-splines - Multiple neural network architectures (DeepONet, TraONet, VaRONet, MambONet) for solving Hamilton's equations - Comparison with traditional numerical methods (Yoshida 4th order, Runge-Kutta 4th order) - Performance evaluation on various physical potentials (harmonic oscillators, double-well potentials, Morse potentials)

Installation

Prerequisites

• Rust & Cargo
• Python 3.8+
• CUDA (optional, for GPU support)
• uv
• just

Setup

1. Clone the repository: bash git clone https://github.com/Axect/Neural_Hamilton cd Neural_Hamilton

2. (Recommended) Setup all dependencies and generate all data in once using just: bash just all

Training Models

The main training script can be run with different dataset sizes: bash python main.py --data normal --run_config configs/deeponet_run_optimized.yaml # 10,000 potentials python main.py --data more --run_config configs/deeponet_run_optimized.yaml # 100,000 potentials

For hyperparameter optimization: bash python main.py --data normal --run_config configs/deeponet_run.yaml --optimize_config configs/deeponet_tpe_full.yaml --device="cuda:0"

Analyzing Results

To analyze trained models: bash python analyze.py

The script provides options to: - Evaluate model performance on test datasets - Generate visualizations of potential functions and trajectories - Compare performance with RK4 numerical solutions

Model Architectures

1. DeepONet: Baseline neural operator model (config example: configs/deeponet_run.yaml) yaml net_config: nodes: 128 layers: 3 branches: 10

2. VaRONet: Variational Recurrent Operator Network (config example: configs/varonet_run.yaml) yaml net_config: hidden_size: 512 num_layers: 4 latent_size: 30 dropout: 0.0 kl_weight: 0.1

3. TraONet: Transformer Operator Network (config example: configs/traonet_run.yaml) yaml net_config: d_model: 64 nhead: 8 num_layers: 3 dim_feedforward: 512 dropout: 0.0

4. MambONet: Mamba Operator Network (config example: configs/mambonet_run.yaml) yaml net_config: d_model: 128 num_layers1: 4 n_head: 4 num_layers2: 4 d_ff: 1024

Key Results

1. Performance Comparison:

   • MambONet consistently outperforms other architectures and RK4
   • Models show improved performance with larger training datasets
   • Neural approaches maintain accuracy over longer time periods compared to RK4

2. Computation Time:

   • TraONet demonstrates fastest computation time
   • MambONet and DeepONet show comparable speeds to RK4
   • VaRONet requires more computational resources

3. Physical Potential Tests:

   • Superior performance on Simple Harmonic Oscillator, Double Well, and Morse potentials
   • Successful extrapolation to non-differentiable potentials (Mirrored Free Fall)
   • Improved accuracy on smoothed variants (Softened Mirrored Free Fall)

Citation

If you use this code in your research, please cite: bibtex @misc{kim2024neuralhamiltonaiunderstand, title={Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?}, author={Tae-Geun Kim and Seong Chan Park}, year={2024}, eprint={2410.20951}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2410.20951}, }

License

MIT License

Acknowledgments

This project uses code from the following repositories:

• mamba.py - Implementation of Mamba and parallel scan used in MambONet
• HyperbolicLR - Implementation of ExpHyperbolicLR scheduler
• SPlus - Implementation of SPlus optimizer