# PySINDy Examples¶

This directory showcases the following examples of PySINDy in action.

## Feature overview¶

This notebook gives an almost exhaustive overview of the different features available in PySINDy. It’s a good reference for how to set various options and work with different types of datasets.

## Introduction to SINDy¶

We recommend that people new to SINDy start here. We give a gentle introduction to the SINDy method and how different steps in the algorithm are represented in PySINDy. We also show how to use PySINDy to learn a model for a simple linear differential equation.

## Original paper¶

This notebook uses PySINDy to reproduce the examples in the original SINDy paper. Namely, it applies PySINDy to the following problems:

Linear 2D ODE

Cubic 2D ODE

Linear 3D ODE

Lorenz system

Fluid wake behind a cylinder

Logistic map

Hopf system

## Scikit-learn compatibility¶

Shows how PySINDy interfaces with various Scikit-learn objects.

Cross-validation

Sparse regressors

## Differentiation¶

Explore the differentiation methods available in PySINDy on pure differentiation problems and as components in the SINDy algorithm.

## Deeptime compatibility¶

See a demonstration of PySINDy objects designed to conform to the Deeptime API.

## Plasma physics¶

Use the `ConstrainedSR3`

optimizer to build a constrained model for the temporal POD modes of a plasma simulation.

## Trapping SINDy¶

This notebook applies the `TrappingSR3`

optimizer to various canonical fluid systems., proposed in this paper: Kaptanoglu, Alan A., et al. “Promoting global stability in data-driven models of quadratic nonlinear dynamics.” Physical Review Fluids 6.9 (2021): 094401. A preprint is found here https://arxiv.org/abs/2105.01843.

## SINDyPI¶

This notebook applies the `SINDyPI`

optimizer to a simple implicit ODE and was originally proposed in this paper: Kaheman, Kadierdan, J. Nathan Kutz, and Steven L. Brunton. “SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics.” Proceedings of the Royal Society A 476.2242 (2020): 20200279.

## PDEFIND¶

This notebook applies the PDEFIND algorithm (SINDy for PDE identification) to a number of PDEs, and was originally proposed in this paper: Rudy, Samuel H., et al. “Data-driven discovery of partial differential equations.” Science Advances 3.4 (2017): e1602614.

## Greedy Algorithms¶

This notebook uses the step-wise sparse regression (SSR) and forward-regression orthogonal least-squares (FROLS) algorithms, which are greedy algorithms that iteratively truncate (or add) one nonzero coefficient at each algorithm iteration.

## Weak formulation SINDy¶

This notebook uses SINDy to identify the weak-formulation of a system of ODEs or PDEs, adding significant robustness against noise in the data.

## Model ensembles¶

This notebook uses sub-sampling of the data and sub-sampling of the SINDy library to generate many models, and the user can choose how to average or otherwise combine these models together. This tends to make SINDy more robust against noisy data.

## Cavity flow¶

Demonstrates the use of SINDy to learn a model for the quasiperiodic dynamics in a shear-driven cavity at Re=7500, following Callaham, Brunton, and Loiseau (2021), preprint available here https://arxiv.org/pdf/2106.02409.

## Full table of contents¶

- Differentiators in PySINDy
- SSR and FROLs (the greedy algorithms!) examples
- Define some functions for plotting performance as the greedy algorithms progress
- Note that the usage is a bit different because we save all the sparse models and we choose our favorite one afterwards. Below we show that we can track the MSE between the predicted and true derivative on a testing trajectory as the algorithm iterates, and then choose the model with the minimum MSE.
- Let’s compare all three methods as the noise steadily increases, cross-validated over 10 noise instantiations
- Summary
- Quasiperiodic cavity flow
- 6. Manifold SINDy
- SINDy-PI Feature Overview
- Example to show library functionality
- Solve 1D Michelson-Menten enzyme model as first demonstration
- Okay, so how to choose which model to use?
- Now we need to reformat all the feature strings for conversion into symbolic variables
- Check all the model fits produce sensible models
- Now we will:
- We will now try a 2D example, the bacterial competence system (Mangan et al. 2016)

- Working with Scikit-learn
- Summary of PySINDy YouTube tutorial videos
- Part 1: How to choose algorithm hyperparameters such as \(\lambda\)?
- Part 2: How to make SINDy more robust for system identification?
- Part 3a: Implicit ODEs
- Part 3b: Identifying partial differential equations (PDEs)
- Part 4: How to choose a regularizer and a sparse regression algorithm?
- Part 5: How to build complex candidate libraries
- An introduction to Sparse Identification of Nonlinear Dynamical systems (SINDy)
- Trapping SINDy
- Mean field model
- Atmospheric oscillator model
- Lorenz model
- Visualizing the trapping region for Lorenz
- MHD model
- Forced Burger’s Equation
- Von Karman shedding behind circular cylinder, Re = 100
- Comparing 3D trajectories with the POD-Galerkin model
- Energy plot illustrating the transient and long-time energy conservation
- Plot \(u(x,y,t)\) reconstructions and modes
- Original Paper: Sparse Identification of Nonlinear Dynamical systems (SINDy)
- PDEFIND Feature Overview
- Define Algorithm 2 from Rudy et al. (2017)
- Test PDE functionality on the 1D kdV equation
- Test spatial derivative computations
- Test PDE functionality on the Kuramoto-Sivashinsky equation
- Test PDE functionality on Burgers’ equation
- Test PDE functionality on 2D Reaction-Diffusion system
- Test PDE functionality on 3D Reaction-Diffusion system
- Ensembling Feature Overview
- Ensemble methods
- The default number of models to generate is 20, and the default sampling strategy is to sample the entire time base with replacement, so that on average 60% or so of the data is seen.
- Default is bragging - the median coefficients over all the models is printed by model.print(). However, model.simulate and model.predict use the coefficients of the last model that was fitted! To avoid confusion, we recommend using model.coef_list everywhere and doing all the post-processing separately.

- Library ensemble methods (V3)
- Now do both types of ensembling with multiple candidate drops (V4)!
- Alternatives to averaging (bagging, P1) or taking the median (bragging, P2) of the models:
- Repeat ensembling but now make ~1000 models and plot the distributions of coefficients
- Plot 95% percentile bounds with mean and median test trajectories
- Variant of library ensembling (P5):
- Generate achievability plots from Maddu et al. 2019
- Test ensemble functionality with weak-PDE functionality, using the Kuramoto-Sivashinsky equation. For understanding weak form use, please see the Example 14 Jupyter Notebook.

- Ensemble methods
- Feature Overview
- A note on solve_ivp vs odeint before we continue
- Train the model
- Assess results on a test trajectory
- Predict derivatives with learned model
- Simulate forward in time
- Single trajectory, pass in collection times
- Single trajectory, set default timestep
- Single trajectory, pass in pre-computed derivatives
- Multiple trajectories
- Multiple trajectories, different lengths of time
- Discrete time dynamical system (map)
- Pandas DataFrame
- STLSQ - change parameters
- STLSQ - verbose (print out optimization terms at each iteration)
- SR3
- SR3 - with trimming
- SR3 regularizers
- SR3 - with variable thresholding
- ConstrainedSR3
- SSR (greedy algorithm)
- FROLS (greedy algorithm)
- LASSO
- Ensemble methods
- Pass in pre-computed derivatives
- Drop end points from finite difference computation
- Differentiation along specific array axis
- Smoothed finite difference
- More differentiation options
- Custom feature names
- Custom left-hand side when printing the model
- Customize polynomial library
- Fourier library
- Fully custom library
- Fully custom library, default function names
- Identity library
- Concatenate two libraries
- Tensor two libraries together
- Generalized library
- Train the model
- Assess results on a test trajectory
- Predict derivatives with learned model
- Simulate forward in time (control input function known)
- Simulate forward in time (unknown control input function)

- PDEFIND Feature Overview
- Plasma physics
- Weak/Integral SINDy Feature Overview
- Test weak form ODE functionality on Lorenz equation
- Test weak form PDE functionality on Burgers’ equation with 20% noise
- Test weak form PDE functionality on nonlinear diffusion with mixed function-derivative terms
- Test weak-formulation PDE functionality on the Kuramoto-Sivashinsky equation
- Test weak form PDE functionality on Reaction-Diffusion system
- Test weak form PDE functionality on 3D Reaction-Diffusion system
- Deeptime objects