We propose a regression method based upon group sparsity that is capable of discovering parametrized governing dynamical equations of motion of a given system by time series measurements. The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. This gives a promising new technique for disambiguating governing equations from simple parametric dependencies in physical, biological and engineering systems.
Data-Driven discovery of governing physical laws and their parametric dependencies in engineering, physics and biology
Alla A.;
2018-01-01
Abstract
We propose a regression method based upon group sparsity that is capable of discovering parametrized governing dynamical equations of motion of a given system by time series measurements. The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. This gives a promising new technique for disambiguating governing equations from simple parametric dependencies in physical, biological and engineering systems.
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