controlled-sde-learn

Overview

controlled-sde-learn is a Python library designed to estimate the coefficients of controlled stochastic differential equations (SDEs). The approach leverages kernel methods and Fokker-Planck equation matching to estimate the drift and diffusion coefficients from a data set of controlled sample paths with random controls.

Examples

The examples folder contains several scripts demonstrating different applications of the controlled-sde-learn library.

1. example_ornstein_uhlenbeck_paths_plot.py. Illustrates the generation and plotting of sample paths from a controlled Ornstein-Uhlenbeck process.
2. example_dubins_paths_plot.py. Illustrates the generation and plotting of sample paths from a controlled Dubins process.
3. example_kde_plot.py. Demonstrates the use of the ProbaDensityEstimator for estimating and visualizing the probability density of sample paths from a controlled SDE under different controls.
4. example_sde_identification_1d.py. Provides a complete example for simulating a one-dimensional controlled SDE and estimating its coefficients using Fokker-Planck matching.
5. example_sde_identification_2d.py. Presents a complete example for estimating the coefficients of a two-dimensional nonlinear controlled SDE.

Explore these examples to understand how to simulate controlled SDEs and estimate their coefficients.

Installation

To install:

1. Clone the repository.

   git clone https://github.com/lmotte/controlled-sde-learn.git

2. Install the required dependencies (Python 3.x required).

   pip install -r requirements.txt

License

This project is licensed under the MIT License - see the LICENSE file for details.