PolyODENet

Inverse chemical kinetics modeling using ODENet.

The initial effort will focus on deriving chemical rate equations from concentration time-series data based on the law of mass action, i.e. systems of first-order ODEs with only polynomial terms on the right-hand side.

The following tools/principles are used.

1. Neural ODE by Ricky T.Q. Chen et al. (https://github.com/rtqichen/torchdiffeq)

2. Symbolic regression

3. Sparse regression

4. Knowledge of kinetic differential equations

Code installation

After download the code, you can do the following

python3 -m venv pon-env
source pon-env/bin/activate
python setup.py develop

This should set 'train_poly' in your $PATH to use.