Automated partial differential equation identification.
J Acoust Soc Am
; 150(4): 2364, 2021 Oct.
Article
de En
| MEDLINE
| ID: mdl-34717467
ABSTRACT
Inspired by recent developments in data-driven methods for partial differential equation (PDE) estimation, we use sparse modeling techniques to automatically estimate PDEs from data. A dictionary consisting of hypothetical PDE terms is constructed using numerical differentiation. Given data, PDE terms are selected assuming a parsimonious representation, which is enforced using a sparsity constraint. Unlike previous PDE identification schemes, we make no assumptions about which PDE terms are responsible for a given field. The approach is demonstrated on synthetic and real video data, with physical phenomena governed by wave, Burgers, and Helmholtz equations. Codes are available at https//github.com/NoiseLab-RLiu/Automate-PDE-identification.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Type d'étude:
Diagnostic_studies
Langue:
En
Journal:
J Acoust Soc Am
Année:
2021
Type de document:
Article
Pays d'affiliation:
États-Unis d'Amérique