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 constitutive behavior of polymeric materials is often modeled by finitelinear viscoelastic (FLV) or quasi-linear viscoelastic (QLV) models. Thesepopular models are simplifications that typically cannot accurately capture thenonlinear viscoelastic behavior of materials. For example, the success ofattempts to capture strain rate-dependent behavior has been limited so far. Toovercome this problem, we introduce viscoelastic Constitutive Artificial NeuralNetworks (vCANNs), a novel physics-informed machine learning framework foranisotropic nonlinear viscoelasticity at finite strains. vCANNs rely on theconcept of generalized Maxwell models enhanced with nonlinear strain(rate)-dependent properties represented by neural networks. The flexibility ofvCANNs enables them to automatically identify accurate and sparse constitutivemodels of a broad range of materials. To test vCANNs, we trained them onstress-strain data from Polyvinyl Butyral, the electro-active polymers VHB 4910and 4905, and a biological tissue, the rectus abdominis muscle. Differentloading conditions were considered, including relaxation tests, cyclictension-compression tests, and blast loads. We demonstrate that vCANNs canlearn to capture the behavior of all these materials accurately andcomputationally efficiently without human guidance.

Viscoelastic Constitutive Artificial Neural Networks (vCANNs) $-$ a framework for data-driven anisotropic nonlinear finite viscoelasticity