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

Differential equations are indispensable to engineering and hence toinnovation. In recent years, physics-informed neural networks (PINN) haveemerged as a novel method for solving differential equations. PINN method hasthe advantage of being meshless, scalable, and can potentially be intelligentin terms of transferring the knowledge learned from solving one differentialequation to the other. The exploration in this field has majorly been limitedto solving linear-elasticity problems, crack propagation problems. This studyuses PINNs to solve coupled thermo-mechanics problems of materials withfunctionally graded properties. An in-depth analysis of the PINN framework hasbeen carried out by understanding the training datasets, model architecture,and loss functions. The efficacy of the PINN models in solving thermo-mechanicsdifferential equations has been measured by comparing the obtained solutionseither with analytical solutions or finite element method-based solutions.While R2 score of more than 99% has been achieved in predicting primaryvariables such as displacement and temperature fields, achieving the same forsecondary variables such as stress turns out to be more challenging. This studyis the first to implement the PINN framework for solving coupledthermo-mechanics problems on composite materials. This study is expected toenhance the understanding of the novel PINN framework and will be seminal forfurther research on PINNs.

Physics-informed neural networks for solving thermo-mechanics problems of functionally graded material