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 / Kutz, J. N.; Rudy, S. H.; Alla, A.; Brunton, S. L.. - 2017-:(2018), pp. 1-5. (Intervento presentato al convegno 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 tenutosi a ant) [10.1109/CAMSAP.2017.8313100].
Data-Driven discovery of governing physical laws and their parametric dependencies in engineering, physics and biology
Alla A.;
2018
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
