DISCOVER: Deep identification of symbolically concise open-form PDEs via enhanced reinforcement-learning
The working mechanisms of complex natural systems tend to abide by concise and profound partial differential equations (PDEs). Methods that directly mine equations from data are called PDE discovery. In this respository, an enhanced deep reinforcement-learning framework is built to uncover symbolically concise open-form PDEs with little prior knowledge.
This repository provides the code and data for following research papers:
(1) DISCOVER: Deep identification of symbolically concise open-form PDEs via enhanced reinforcement-learning. PDF
(2) Physics-constrained robust learning of open-formPDEsfrom limited and noisy data. PDF
conda create -n env_name python=3.7 # Create a Python 3 virtual environment with conda.
source activate env_name # Activate the virtual environment
From the root directory,
pip install --upgrade setuptools pip
export CFLAGS="-I $(python -c "import numpy; print(numpy.get_include())") $CFLAGS" # Needed on Mac to prevent fatal error: 'numpy/arrayobject.h' file not found
pip install -e ./dso # Install package and core dependencies
Extra dependencies,
pip install -r requirements.txt # Possible incompatibility may occurs due to the version of tensorboard. Manually installing it may be required.
pip install tensorboard
There are two executation modes in DISCOVER for dealing with different applications.
The first mode is designed for discoving PDEs from high-quality data. Partial derivatives are evaluated by numerical differentiation on regular grids. DNN can be optionally utilized to smoothe available data and generate meta data to reduce the impact of noise. The introduction of the whole framework can be found in the first paper PDF. GPU is not necessary since the matrix calculation is based on Numpy.
The second mode originates from a robust verison of DISCOVER, named R_DISCOVER, which is designed to handle sparse and noisy data. A NN is utilized to fit the system response and evaluate the reward by automatic differentiation. It is trained in a PINN manner when effective physical information are discovered. This mode is more suitable for the high-noisy scenarios. The introduction of the whole framework can be found in the second paper PDF. GPU resources are required to acclerate the searching process.
For the first mode, several benchmark datasets are provided, including Chafee-Infante equation, KdV equations and PDE_divide, etc. Run the script below can repeat the results in the first paper.
sh ./script_test/MODE1_test.sh
For the second mode, Burgers equation is taken as an example. More examples will be supplemented in the future.
sh ./script_test/MODE2_test.sh
- Step 1: Put the dataset in the specified directory and write the data loading module. The default directory for benchmark datasets is './dso/dso/task/pde/data_new'. The function of load_data for loading benchmark datasets is located at './dso/dso/task/pde/data_load.py'.
def load_data(data_path='./dso/task/pde/data_new/Kdv.mat'):
""" load dataset in class PDETask"""
data = scio.loadmat(data_path)
u=data.get("uu")
n,m=u.shape
x=np.squeeze(data.get("x")).reshape(-1,1)
t=np.squeeze(data.get("tt").reshape(-1,1))
n,m = u.shape #512, 201
dt = t[1]-t[0]
dx = x[1]-x[0]
# true right-hand-side expressions
sym_true = 'add,mul,u1,diff,u1,x1,diff3,u1,x1'
n_input_var = 1 # space dismension
n_state_var = 1 # number of the state variable
X=[] # define the space vector list, inlcuding x,y,...
test_list =None
ut = np.zeros((n, m)) # define the left-hand-side of the PDE
dt = t[1]-t[0]
X.append(x)
for idx in range(n):
ut[idx, :] = FiniteDiff(u[idx, :], dt)
return [u],X,t,ut,sym_true, n_input_var,test_list,n_state_var-
Step 2: Hyperparameter setting. All of hyperparameters are passed to the class DeepSymbolicOptimizer_PDE through a JSON file. The default parameter setting is located at './dso/dso/config/config_pde.json'. Users can define their parameters according to the example in the benchmark dataset './dso/dso/config/MODE1'.
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Step 3: Execute the PDE discovery task. Output and save results. An example is shown in './dso/test_pde.py'.
from dso import DeepSymbolicOptimizer_PDE
import pickle
data_name = 'KdV'
config_file_path = "./dso/config/MODE1/config_pde_KdV.json"
# build model by passing the path of user-defined config file.
model = DeepSymbolicOptimizer_PDE(config_file_path)
# model training
result = model.train()
#save results
with open(f'{data_name}.pkl', 'wb') as f:
pickle.dump(result, f)
(1) Petersen et al. 2021 Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients. ICLR 2021. Paper
(2) Mundhenk, T., Landajuela, M., Glatt, R., Santiago, C. P., & Petersen, B. K. (2021). Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding. Advances in Neural Information Processing Systems, 34, 24912-24923. Paper
The code of this repository is developed specifically for PDE discovery tasks based on the framework of DSO. This repository is not available for commercial use.