GOKU - Deep Generative ODE Modelling with Known Unknowns

This repository is an implementation of the GOKU paper: Generative ODE Modeling with Known Unknowns.

Data creation

To create the datasets used in the paper run:

• Friction-less pendulum: python3 create_data.py --model pendulum
• Friction pendulum: python3 create_data.py --model pendulum --friction
• Double-pendulum experiment: python3 create_data.py --model double_pendulum
• Cardiovascular system: python3 create_data.py --model cvs

The data would be created using default arguments. To view / modify them check the file config.py, and create_data.py.

Training

To train the GOKU model run: python3 goku_train.py --model <pendulum/pendulum_friction/double_pendulum/cvs>

To train baselines:

• Latent-ODE: python3 latent_ode_train.py --model <pendulum/pendulum_friction/double_pendulum/cvs>.
• LSTM: python3 lstm_train.py --model <pendulum/pendulum_friction/double_pendulum/cvs>.
• Direct-Identification (DI) has 3 different files for the different datasets (it cannot run the friction pendulum, since it needs the entire ODE functional form):
  • Pendulum: python3 di_baseline_pendulum.py
  • Double Pendulum: python3 di_baseline_double_pendulum.py
  • CVS: python3 di_baseline_cvs.py

Requirements:

• python 3
• pytorch
• numpy
• gym (for the pendulum and double pendulum experiments)