English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The measured spatiotemporal response of various physical processes isutilized to infer the governing partial differential equations (PDEs). Wepropose SimultaNeous Basis Function Approximation and Parameter Estimation(SNAPE), a technique of parameter estimation of PDEs that is robust againsthigh levels of noise nearly 100 %, by simultaneously fitting basis functions tothe measured response and estimating the parameters of both ordinary andpartial differential equations. The domain knowledge of the generalmultidimensional process is used as a constraint in the formulation of theoptimization framework. SNAPE not only demonstrates its applicability onvarious complex dynamic systems that encompass wide scientific domainsincluding Schr\"odinger equation, chaotic duffing oscillator, and Navier-Stokesequation but also estimates an analytical approximation to the processresponse. The method systematically combines the knowledge of well-establishedscientific theories and the concepts of data science to infer the properties ofthe process from the observed data.

Data-Driven Theory-guided Learning of Partial Differential Equations  using SimultaNeous Basis Function Approximation and Parameter Estimation  (SNAPE)