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.
2018
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3746327
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 10
  • ???jsp.display-item.citation.isi??? 6
social impact