# Research Neural ODE Implementations

This directory contains advanced neural ODE implementations for research applications.

## Files

- `hybrid_neural_ode_original.py`: Original hybrid implementation with JAX/PyTorch support
- `hybrid_neural_ode_fixed.py`: Fixed hybrid implementation with dtype consistency

## Usage

These implementations are designed for research applications that require:
- Multiple backend support (JAX/PyTorch/NumPy)
- Advanced fractional calculus
- Multi-scale temporal dynamics
- Physics-informed neural ODEs

## Performance Note

These implementations prioritize features over performance. For production use,
see the main `neural_ode.py` implementation.

## Author

Davian R. Chin, Department of Biomedical Engineering, University of Reading
