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 concepts and techniques of physics-informed neural networks (PINNs) isstudied and limitations are identified to make it efficient to approximatedynamical equations. Potential working research domains are explored forincreasing the robustness of this technique for the solvability of partialdifferential equations. It is identified that PINNs potentially fails tostronger advection and longer time duration. Also, optimization function andconstraint posing needs to be smarter. Even a shallow network is good for a lotof problems while powerful deeper network fails. Reservoir computing basedrecurrent neural network architecture is recommended to solve dynamicalproblems.

PINN for Dynamical Partial Differential Equations is Not Training Deeper Networks Rather Learning Advection and Time Variance