All Outputs (3)

Automating the discovery of partial differential equations in dynamical systems (2024)
Journal Article
Li, W., & Carvalho, R. (2024). Automating the discovery of partial differential equations in dynamical systems. Machine Learning: Science and Technology, 5(3), Article 035046. https://doi.org/10.1088/2632-2153/ad682f

Identifying partial differential equations (PDEs) from data is crucial for understanding the governing mechanisms of natural phenomena, yet it remains a challenging task. We present an extension to the ARGOS framework, ARGOS-RAL, which leverages spar... Read More about Automating the discovery of partial differential equations in dynamical systems.

Automatically discovering ordinary differential equations from data with sparse regression (2024)
Journal Article
Egan, K., Li, W., & Carvalho, R. (2024). Automatically discovering ordinary differential equations from data with sparse regression. Communications Physics, 7(1), Article 20. https://doi.org/10.1038/s42005-023-01516-2

Discovering nonlinear differential equations that describe system dynamics from empirical data is a fundamental challenge in contemporary science. While current methods can identify such equations, they often require extensive manual hyperparameter t... Read More about Automatically discovering ordinary differential equations from data with sparse regression.

Automatically identifying ordinary differential equations from data (2023)
Report
Egan, K., Li, W., & Carvalho, R. (2023). Automatically identifying ordinary differential equations from data. Durham University

Discovering nonlinear differential equations that describe system dynamics from empirical data is a fundamental challenge in contemporary science. Here, we propose a methodology to identify dynamical laws by integrating denoising techniques to smooth... Read More about Automatically identifying ordinary differential equations from data.