Lorenz SBI

Repository for running simulation based inference (SBI) pipeline on the simple Lorenz system. The Lorenz system has 3 parameters ($\sigma$, $\beta$, and $\rho$) and a set of summary statistics (mean, covariance, correlation, eigvalues, lyapunov exponents) based on which the neural network will train. This repository is made for educational purposes and serves as a starting point to implement your own models!

The figure shows an example inferred posterior. The real parameters are: $\sigma = 10$, $\beta = \frac{8}{3}$, and $\rho = 28$. The trained network was able to recover these parameters using only the observation statistics.

Installation on Linux

Create virtual environment

python venv -m env_name

Activate environment

source env_name/bin/activate

Install requirements

pip install -r requirements.txt

Usage

The full pipeline for inference is given here:

1. Run simulations

To run simulations in parallel on multiple CPU cores run the following code:

mpirun -n num_cores python cpu_sims.py

2. Train density estimator

To train a neural density estimator on the CPU run the following lineL

python3 train.py --device cpu

To train on GPU run:

python3 train.py --device cuda

3. Get posterior

The following line will generate a pairplot figure of the full posterior given the observation statistics of the Lorenz system with parameters: $\sigma = 10$, $\beta = \frac{8}{3}$, and $\rho = 28$.

python3 posterior.py

Many settings can be altered. This repository is made for educational purposes and serves as a starting point to implement your own models.

krisevers / lorenz_sbi