# Sparse identification of nonlinear dynamics in the presence of library and system uncertainty

arXiv: https://arxiv.org/abs/2401.13099 · HTML: https://mlsanity.com/paper/2401.13099/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 23 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics](https://mlsanity.com/paper/2004.02322/) (2020)
- [Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders](https://mlsanity.com/paper/2201.05136/) (2022)
- [A unified sparse optimization framework to learn parsimonious physics-informed models from data](https://mlsanity.com/paper/1906.10612/) (2019)
- [Investigating Sindy As a Tool For Causal Discovery In Time Series Signals](https://mlsanity.com/paper/2212.14133/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Robust SINDy Autoencoder for Noisy Dynamical System Identification](https://mlsanity.com/paper/2604.04829/) (2026)

## Abstract (arXiv)

The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. However, SINDy assumes the user has prior knowledge of the variables in the system and of a function library that can act as a basis for the system. In this paper, we demonstrate on real world data how the Augmented SINDy algorithm outperforms SINDy in the presence of system variable uncertainty. We then show SINDy can be further augmented to perform robustly when both kinds of uncertainty are present.