Using dropout based active learning and surrogate models in the inverse viscoelastic parameter identification of human brain tissue
Inverse mechanical parameter identification enables the characterization of ultrasoft materials, for which it is difficult to achieve homogeneous deformation states. However, this usually involves high computational costs that are mainly determined by the complexity of the forward model. While simul...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Physiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2024.1321298/full |
| _version_ | 1797348584398520320 |
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| author | Jan Hinrichsen Carl Ferlay Carl Ferlay Nina Reiter Silvia Budday |
| author_facet | Jan Hinrichsen Carl Ferlay Carl Ferlay Nina Reiter Silvia Budday |
| author_sort | Jan Hinrichsen |
| collection | DOAJ |
| description | Inverse mechanical parameter identification enables the characterization of ultrasoft materials, for which it is difficult to achieve homogeneous deformation states. However, this usually involves high computational costs that are mainly determined by the complexity of the forward model. While simulation methods like finite element models can capture nearly arbitrary geometries and implement involved constitutive equations, they are also computationally expensive. Machine learning models, such as neural networks, can help mitigate this problem when they are used as surrogate models replacing the complex high fidelity models. Thereby, they serve as a reduced order model after an initial training phase, where they learn the relation of in- and outputs of the high fidelity model. The generation of the required training data is computationally expensive due to the necessary simulation runs. Here, active learning techniques enable the selection of the “most rewarding” training points in terms of estimated gained accuracy for the trained model. In this work, we present a recurrent neural network that can well approximate the output of a viscoelastic finite element simulation while significantly speeding up the evaluation times. Additionally, we use Monte-Carlo dropout based active learning to identify highly informative training data. Finally, we showcase the potential of the developed pipeline by identifying viscoelastic material parameters for human brain tissue. |
| first_indexed | 2024-03-08T12:08:08Z |
| format | Article |
| id | doaj.art-d952d27d74804e35bcffe060f9d62f34 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-03-08T12:08:08Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-d952d27d74804e35bcffe060f9d62f342024-01-23T04:42:53ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2024-01-011510.3389/fphys.2024.13212981321298Using dropout based active learning and surrogate models in the inverse viscoelastic parameter identification of human brain tissueJan Hinrichsen0Carl Ferlay1Carl Ferlay2Nina Reiter3Silvia Budday4Institute of Continuum Mechanics and Biomechanics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, GermanyInstitute of Continuum Mechanics and Biomechanics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, GermanyEcole Polytechnique, Palaiseau, FranceInstitute of Continuum Mechanics and Biomechanics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, GermanyInstitute of Continuum Mechanics and Biomechanics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, GermanyInverse mechanical parameter identification enables the characterization of ultrasoft materials, for which it is difficult to achieve homogeneous deformation states. However, this usually involves high computational costs that are mainly determined by the complexity of the forward model. While simulation methods like finite element models can capture nearly arbitrary geometries and implement involved constitutive equations, they are also computationally expensive. Machine learning models, such as neural networks, can help mitigate this problem when they are used as surrogate models replacing the complex high fidelity models. Thereby, they serve as a reduced order model after an initial training phase, where they learn the relation of in- and outputs of the high fidelity model. The generation of the required training data is computationally expensive due to the necessary simulation runs. Here, active learning techniques enable the selection of the “most rewarding” training points in terms of estimated gained accuracy for the trained model. In this work, we present a recurrent neural network that can well approximate the output of a viscoelastic finite element simulation while significantly speeding up the evaluation times. Additionally, we use Monte-Carlo dropout based active learning to identify highly informative training data. Finally, we showcase the potential of the developed pipeline by identifying viscoelastic material parameters for human brain tissue.https://www.frontiersin.org/articles/10.3389/fphys.2024.1321298/fullactive learningneural networksurrogate modelparameter identificationhuman brain tissue |
| spellingShingle | Jan Hinrichsen Carl Ferlay Carl Ferlay Nina Reiter Silvia Budday Using dropout based active learning and surrogate models in the inverse viscoelastic parameter identification of human brain tissue Frontiers in Physiology active learning neural network surrogate model parameter identification human brain tissue |
| title | Using dropout based active learning and surrogate models in the inverse viscoelastic parameter identification of human brain tissue |
| title_full | Using dropout based active learning and surrogate models in the inverse viscoelastic parameter identification of human brain tissue |
| title_fullStr | Using dropout based active learning and surrogate models in the inverse viscoelastic parameter identification of human brain tissue |
| title_full_unstemmed | Using dropout based active learning and surrogate models in the inverse viscoelastic parameter identification of human brain tissue |
| title_short | Using dropout based active learning and surrogate models in the inverse viscoelastic parameter identification of human brain tissue |
| title_sort | using dropout based active learning and surrogate models in the inverse viscoelastic parameter identification of human brain tissue |
| topic | active learning neural network surrogate model parameter identification human brain tissue |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2024.1321298/full |
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