| --- |
| library_name: staticplay-curiodynamics |
| license: mit |
| tags: |
| - physics |
| - symbolic-regression |
| - curiosity |
| - dynamics |
| - discovery |
| - scientific-ml |
| --- |
| |
| # Staticplay CurioDynamics |
|
|
| Curiosity-driven symbolic physics discovery from raw trajectories (position, velocity, time). The agent observes only state and learns a sparse symbolic model that recovers hidden dynamics such as gravity, drag, and wind. |
|
|
| ## What It Does |
| - Generates a hidden physics world (projectile motion with drag + wind). |
| - Observes only `position`, `velocity`, and `time`. |
| - Learns a symbolic acceleration model via sparse regression. |
| - Recovers interpretable formulas like: |
| - `ax = wind − k * vx * |vx|` |
| - `ay = −g − k * vy * |vy|` |
|
|
| ## Quick Start |
| Requires Python 3.10+. |
|
|
| ```powershell |
| python .\physics_world.py |
| ``` |
|
|
| ## CLI Usage |
| ```powershell |
| python .\physics_world.py --episodes 50 --steps 200 --g 9.81 --drag 0.12 --wind 0.4 --drag_power 2.0 --dt 0.1 --seed 123 |
| ``` |
|
|
| ## How It Works |
| 1. Simulates a hidden world with gravity + drag + wind. |
| 2. Collects state transitions only (no equations given). |
| 3. Fits a sparse symbolic model over a small feature library. |
| 4. Interprets the recovered constants as wind and gravity. |
|
|
| ## Latest Test Results |
| Hidden world: quadratic drag + wind |
|
|
| ``` |
| g_true = 9.81 |
| drag_true = 0.12 |
| wind_true = 0.4 |
| |
| model_ax: ax = 0.4 − 0.12 * vx|vx| |
| model_ay: ay = −9.81 − 0.12 * vy|vy| |
| wind_est = 0.4 |
| g_est = 9.81 |
| ``` |
|
|
| ## Example Output |
| ```text |
| {'g_true': 9.81, 'drag_true': 0.12, 'wind_true': 0.4, 'drag_power_true': 2.0, |
| 'model_ax': {'vx_abs_vx': -0.12, '1': -2.209}, |
| 'model_ay': {'vy_abs_vy': -0.12, '1': -13.034}, |
| 'wind_est': 0.4, 'g_est': 9.81, 'samples': 634} |
| ``` |
|
|
| ## Install |
| No external dependencies beyond standard library. |
|
|
| If you want a virtual environment: |
| ```powershell |
| python -m venv .venv |
| .\.venv\Scripts\activate |
| python .\physics_world.py |
| ``` |
|
|
| ## Files |
| - `physics_world.py`: hidden physics world + curiosity agent + symbolic regression |
|
|
| ## How To Run Different Scenarios |
| Edit `run_stress_test(...)` in `physics_world.py`: |
| - `wind` |
| - `drag` |
| - `drag_power` |
| - `episodes` |
| - `steps` |
|
|
| Example: |
| ```python |
| result = run_stress_test( |
| episodes=50, |
| steps=200, |
| g=9.81, |
| drag=0.12, |
| wind=0.4, |
| drag_power=2.0, |
| dt=0.1 |
| ) |
| print(result) |
| ``` |
|
|
| ## Notes |
| This is intentionally minimal and deterministic to highlight discovery mechanics. The feature library is constrained to keep the model interpretable. |
|
|
| ## Acknowledgements |
| - teliov/symcat-to-synthea |
|
|
| ## Links |
| - https://staticplay.co.uk |
|
|
