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Found 3 papers, 2 papers with code
Automating the Discovery of Partial Differential Equations in Dynamical Systems
Identifying partial differential equations (PDEs) from data is crucial for understanding the governing mechanisms of natural phenomena, yet it remains a challenging task.
Automatically identifying ordinary differential equations from data
Discovering nonlinear differential equations that describe system dynamics from empirical data is a fundamental challenge in contemporary science.
Explaining the distribution of energy consumption at slow charging infrastructure for electric vehicles from socio-economic data
We identified the most influential features correlated with energy consumption, indicating that the spatial context of the charging infrastructure affects its utilization pattern.
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