jiangnanhugo/APPS-ODE

README: Active Discovery of Ordinary Differential Equations via Phase Portrait Sketching

1. Prerequisite of using these methods

• install the dependency package

pip install -r requirements.txt

2. Directory

Dataset

• data_oracle: the generated dataset. Every file represents a ground-truth expression.
• data_oracle/scibench: the data oracle API to draw data. Before you run our program, you need to install the dataoracle by

cd data_oracle/scibench
pip install -e .

Methods

• apps_ode_pytorch: the proposed method.
• baselines/ProGED: from https://github.com/brencej/ProGED.
• baselines/SPL: symbolic physics learner, from https://github.com/isds-neu/SymbolicPhysicsLearner.
• baselines/E2E: End to end transformer for symbolic regression, from https://github.com/facebookresearch/symbolicregression.
• baselines/odeformer: ODEFormer: Symbolic Regression of Dynamical Systems with Transformers.

Extra

• plots: the jupyter notebook to generate our figure.
• result: contains all the output of all the programs, the training logs.

3. Look at the summarized result

The experimental results are summarized in the result and plots folders.