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# Data-Driven Symbolic Equation Discovery of Multi-Robot Dynamics

This repository presents a physics-informed machine learning pipeline for the analysis of multi-robot trajectory data and the discovery of interpretable mathematical models describing their dynamics.

The workflow combines:
* Trajectory preprocessing
* Hyperparameter optimization with **Optuna**
* Symbolic regression via **EPDE**.

## Project Goal

The goal of this work is to develop a hyperparameter optimization with **Optuna** for identifying accurate, compact, and interpretable systems of ordinary differential equations (ODEs) governing the motion of robots based on experimental trajectory data.

---
## Data Description

* **Pickle file** contains raw experimental data extracted from video tracking.
* For each robot and timestep:
* robot ID,
* 2D coordinates `(x, y)`.
* Supported robot shapes:
* circle
* oval

---
## Components

`DataProcessor`
* Extracts coordinates
* Normalizes trajectories (MinMax scaling)

`discovery_science.ipynb`
* Splits trajectories into segments
* Runs EPDE for symbolic regression
* Uses Optuna for hyperparameter tuning
* Outputs differential equations

---
## Hyperparameter optimization

For each trajectory segment of `n_parts` independently:

* **Optuna** optimizes EPDE parameters:

* polynomial window,
* smoothing sigma,
* boundary,
* population size.

* **Optuna** optimizes TEDEOUS parameters:

* the number and frequency of Fourier transform embeddings,
* the number of layers

* Objective balances:

* equation residuals,
* model complexity,
* reconstruction stability.
---

## Repository Structure

```
.
├── discovery_science_mezo.ipynb
├── data_process.py
├── {circle|oval}/
│ ├── data/
│ └── {circle_data_00_330_[30_bots_PWM_10_15cw_15ccw_D_41cm].MP4.pickle | oval_data_[30_bots_PWM_1_exp_1].pickle}
│ ├── levels_robots_ids.json
│ ├── EPDE_output_micro/
│ ├── robot_{id}/
│ └── {n}_parts/
│ └── {with|without}_force/
│ ├── part_{nk}_system_best_params.json
│ ├── part_{nk}_system_history_plot.html
│ ├── part_{nk}_system_importances_plots.html
│ ├── system_{nk}.csv
│ └── part_{nk}_obj_func_and_equations.txt
│ └── EPDE_output_meso
│ ├── robot_{ids}
│ └── ...
└── README.md
```
---

## Dependencies

Core libraries:
* numpy, pandas, scipy

Machine Learning
* scikit-learn

Optimization & Discovery
* epde
* optuna

Utility
* dill (pickle)
* pathlib
* json

---
## Research Outcomes
* Developed a unified pipeline for data-driven discovery of dynamical systems

---

## Limitations and Assumptions

* Robot ID < 100
* Only `oval` and `circle` supported
* derivatives up to second order
* EPDE sensitive to noise and hyperparameters

---

For more information, see the repository https://github.com/20saaa02/Active_Matter






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{
"best_params": {
"poly_window": 11,
"population_size": 14,
"sigma": 5,
"boundary": 7
},
"score": 155.68504506502222
}

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{
"best_params": {
"poly_window": 11,
"population_size": 14,
"sigma": 2,
"boundary": 7
},
"score": 107.78402244748412
}

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{
"best_params": {
"poly_window": 5,
"population_size": 14,
"sigma": 0,
"boundary": 4
},
"score": 9.424775437701731
}

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