SlowFastSeparation

A python implementation of the KDD'23 paper: "Learning Slow and Fast System Dynamics via Automatic Separation of Time Scales".

Requirements

• Python 3.10
• PyTorch==1.12
• scikit-learn==1.1.2
• Numpy
• Scipy
• Matplotlib
• tqdm
• scikit-dimension
• torchdiffeq
• torchsummary

Usage

Our Model

Phase1: Selecting the appropriate time scale $\tau_s$ and slow dimension slow_dim by ID-driven method.

# switch --phase to 'TimeSelection'
./OURS.sh

Phase2: Separating the fast and slow components and learning the dynamics.

# switch --phase to 'LearnDynamics'
# choose appropriate params(--tau_s, --slow_dim and --koopman_dim) by the Phase1
./OURS.sh

Baseline

Train and test models in 1S2F and 2S2F system:

./LSTM.sh # for LSTM
./TCN.sh # for TCN
./NeuralODE # for Neural ODE

We recommend turning on the --parallel option to enable parallel execution of programs with different random seeds to improve test efficiency. Please be careful to choose the suitable number of random seeds --seed_num according to your computational and cache resources. The result of the experiment should be an average of multiple random seeds.