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ZENODO
Project deliverable . 2021
License: CC BY
Data sources: Datacite
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Project deliverable . 2021
License: CC BY
Data sources: Datacite
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Efficient Meta-modelling for the simulation of soft tissue deformations

Authors: Kerfriden, Pierre; Saurabh Deshpande; Mikaeili, Ehsan;

Efficient Meta-modelling for the simulation of soft tissue deformations

Abstract

This work has been done as a part of scientific deliverable 4.1 of ITN Rainbow.(https://rainbow.ku.dk). A. In first part program is written for predicting the position of brain tumour in brain surgery. In the surgery, as surgeonmakes incision and opens the skull, the brain tumour relocates under new boundary conditions. Predicting the position of tumor under different incision sizes is invaluable data for surgeons, which is done by using gaussian process regressor meta-model. B. Second part shows the potential of deep learning approach to solve non-linear deformation behaviour of a cantilever beam in real time constraints. It is shown that deep learning model is able to predict the solution in a very less time compared to finite element solution time and yet maintaining the reasonable accuracy.

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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