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Symmetry-Informed Governing Equation Discovery

The official codebase for our paper (NeurIPS 2024 Poster), Symmetry-Informed Governing Equation Discovery.

pipeline

Data preparation

Option 1: download data

We provide the generated data used in our experiments at this link.

Unzip the data and put all the files in the ./data subdirectory.

Option 2: generate data

Alternatively, generate the data using the scripts in the data_utils directory. This allows for more flexibility in altering the noise scale, number of trajectories, sampling interval, etc. The general format:

python -m data_utils.${ODE_SYSTEM_NAME} --n_ics ${N_ICS} --num_steps ${N_STEPS} --noise ${NOISE_SCALE} --dt ${DELTA_T} --smoothing ${SMOOTHING_METHOD} --save_dir ${SAVE_DIR} --save_name ${SAVE_NAME}

where the arguments stand for:

  • n_ics: number of trajectories (each with a random initial condition)
  • num_steps: number of timesteps within each trajectory
  • dt: time interval between adjacent timesteps
  • smoothing: smoothing method for noisy data, only gp (Gaussian process) or None supported currently
  • save_dir: the directory where the data file is saved
  • save_name: name for the data file; note that the final name also includes some other configurations

For more details, see the Python scripts in the data_utils directory.

Below we provide examples for the ODE systems used in the paper:

  • Damped Oscillator
python -m data_utils.damped_oscillator --n_ics 50 --noise 0.2 --smoothing gp
python -m data_utils.damped_oscillator --n_ics 10 --noise 0.2 --smoothing gp --save_name val
  • Growth
python -m data_utils.growth --n_ics 100 --noise 0.05 --smoothing gp
python -m data_utils.growth --n_ics 20 --noise 0.05 --smoothing gp --save_name val
  • Lotka-Volterra System
python -m data_utils.lotka --n_ics 200 --noise 0.99 --smoothing gp
python -m data_utils.lotka --n_ics 20 --noise 0.99 --smoothing gp --save_name val
  • Glycolytic Oscillator
python -m data_utils.selkov --n_ics 10 --noise 0.2 --smoothing gp
python -m data_utils.selkov --n_ics 2 --noise 0.2 --smoothing gp --save_name val

Equation Discovery

Dependency

For experiments that do not involve genetic programming:

wandb==0.14.0
sympy==1.13.0
numpy=1.23.5
pytorch==1.13.1
tqdm==4.64.1

For genetic programming, we require additional dependency due to the use of PySR package and the need of precomputing the symmetry regularization with some beta PyTorch feature in torch.func. Create the conda environment by

conda env create -f envs/pt_beta.yml

Before running, replace the wandb project and entity with your information in main.py, main_sindy.py and main_wsindy.py, or simply export WANDB_MODE=disabled to disable wandb logging.

Equivariant SINDy for Linear Symmetries (Section 5.1)

  • Damped Oscillator
# EquivSINDy-c (ours)
bash run_scripts/dosc_noise20_esindy.sh
# SINDy
bash run_scripts/dosc_noise20_sindy.sh
# Weak SINDy
bash run_scripts/dosc_noise20_wsindy.sh
# Genetic Programming w/ PySR
python main_pysr.py --config dosc/noise20_pysr.cfg
  • Growth
# EquivSINDy-c (ours)
bash run_scripts/growth_noise05_esindy.sh
# SINDy
bash run_scripts/growth_noise05_sindy.sh
# Weak SINDy
bash run_scripts/growth_noise05_wsindy.sh
# Genetic Programming w/ PySR
python main_pysr.py --config growth/noise05_pysr.cfg

Equivariant SINDy in Latent Space (Section 5.2)

First, place the data file reaction_diffusion.mat in the data directory ./data. Then, to simultaneously discover symmetry and an equivariant equation,

python main.py --config rd/sym_eq.cfg

Symmetry Regularization (Section 5.3)

  • Lotka-Volterra System
# Symmetry Discovery (run this before any equivariant method)
python main.py --config lv/noise99_sym.cfg

# Sparse regression
# SINDy
bash run_scripts/lv_noise99_eq_sindy.sh
# WSINDy
bash run_scripts/lv_noise99_eq_wsindy.sh
# EquivSINDy-r
bash run_scripts/lv_noise99_eq_ireg.sh

# Genetic programming
mkdir pysr_temp
# PySR
python main_pysr.py --config lv/noise99_eq_pysr.cfg
# EquivGP-r (ours)
python main_pysr.py --config lv/noise99_eq_pysr_symm.cfg
  • Glycolytic Oscillator (Sel'Kov)
# Symmetry Discovery (run this before any equivariant method)
python main.py --config selkov/noise20_sym.cfg

# Sparse regression
# SINDy
bash run_scripts/selkov_noise20_eq_sindy.sh
# WSINDy
bash run_scripts/selkov_noise20_eq_wsindy.sh
# EquivSINDy-r (ours)
bash run_scripts/selkov_noise20_eq_symreg.sh

# Genetic programming
mkdir pysr_temp
# PySR
python main_pysr.py --config selkov/noise20_eq_pysr.cfg
# EquivGP-r (ours)
python main_pysr.py --config selkov/noise20_eq_pysr_symm.cfg

Evaluation

For sparse-regression-based approaches, we provide an evaluation pipeline to compute the success probability and parameter estimation errors aggregated from multiple runs. See notebooks/eval_results.ipynb for details.

Citation

@inproceedings{yang2024symmetry,
  title={Symmetry-Informed Governing Equation Discovery}, 
  author={Yang, Jianke and Rao, Wang and Dehmamy, Nima and Walters, Robin and Yu, Rose},
  year={2024},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)}, 
  url={https://arxiv.org/pdf/2405.16756},
}