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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We study the feasibility of data based machine learning applied to ultrasoundtomography to estimate water-saturated porous material parameters. In thiswork, the data to train the neural networks is simulated by solving wavepropagation in coupled poroviscoelastic-viscoelastic-acoustic media. As theforward model, we consider a high-order discontinuous Galerkin method whiledeep convolutional neural networks are used to solve the parameter estimationproblem. In the numerical experiment, we estimate the material porosity andtortuosity while the remaining parameters which are of less interest aresuccessfully marginalized in the neural networks-based inversion. Computationalexamples confirms the feasibility and accuracy of this approach.

Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography