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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This study presents a systematic machine learning approach for creatingefficient hybrid models and discovering sorption uptake models in non-linearadvection-diffusion-sorption systems. It demonstrates an effective method totrain these complex systems using gradient based optimizers, adjointsensitivity analysis, and JIT-compiled vector Jacobian products, combined withspatial discretization and adaptive integrators. Sparse and symbolic regressionwere employed to identify missing functions in the artificial neural network.The robustness of the proposed method was tested on an in-silico data set ofnoisy breakthrough curve observations of fixed-bed adsorption, resulting in awell-fitted hybrid model. The study successfully reconstructed sorption uptakekinetics using sparse and symbolic regression, and accurately predictedbreakthrough curves using identified polynomials, highlighting the potential ofthe proposed framework for discovering sorption kinetic law structures.

Efficient hybrid modeling and sorption model discovery for non-linear  advection-diffusion-sorption systems: A systematic scientific machine  learning approach