Author

K. Linka

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 8 Biophysics· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 8 Organism:human· 6 Research Focus:benchmarking· 5 Approach:experimental assay· 2 Approach:statistical model· 2 Assay:metabolomics· 1

Frequent Co-Authors

E. Kuhl· 9 shared S. R. St. Pierre· 4 shared G. A. Holzapfel· 3 shared M. Peirlinck· 3 shared E. C. Darwin· 2 shared J. A. Hurtado· 2 shared M. E. Levenston· 2 shared A. Buganza Tepole· 1 shared

Activity by Year

2022-2024 · peak 6/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

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9 shared papersBioengineering

E. Kuhl

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Author profile →

9 papers

Recent·Most cited

The mechanical and sensory signature of plant-based and animal meat

S. R. St. Pierre, E. C. Darwin, D. Adil, M. C. Aviles, A. M. Date, R. Dunne, Y. Lall, M. P. Vallecillo, V. A. P. Medina, K. Linka, M. E. Levenston, E. Kuhl

The paper measures the mechanical and sensory properties of plant-based and animal meats using multi-dimensional tests and neural networks, finding that some plant-based products closely mimic animal meats in texture.

bioRxiv · Apr 25, 2024Bioengineering

Automated model discovery for human cardiac tissue: Discovering the best model and parameters

D. Martonova, M. Peirlinck, K. Linka, G. A. Holzapfel, S. Leyendecker, E. Kuhl

This paper automates cardiac tissue model discovery by training a neural network to identify the best model and parameters for human cardiac tissue.

bioRxiv · Feb 27, 2024Bioengineering

Democratizing biomedical simulation through automated model discovery and a universal material subroutine

M. Peirlinck, K. Linka, J. A. Hurtado, G. A. Holzapfel, E. Kuhl

This paper automates material model selection in finite element simulations using constitutive neural networks, improving robustness and accessibility of biomedical simulations.

bioRxiv · Dec 6, 2023Bioengineering

On automated model discovery and a universal material subroutine

M. Peirlinck, K. Linka, J. A. Hurtado, E. Kuhl

This paper automates model selection for constitutive modeling in finite element analysis, demonstrating a universal material subroutine that generalizes to multiple models and accurately predicts stress in human brain tissue.

bioRxiv · Jul 19, 2023 · PreprintBioengineering

Discovering the mechanics of artificial and real meat

S. R. St. Pierre, D. Rajasekharan, E. C. Darwin, K. Linka, M. E. Levenston, E. Kuhl

This paper compares the mechanical properties of artificial and real meat using tension, compression, and shear tests, finding that artificial chicken matches real chicken in stress values but tofurky is stiffer and less shear-resistant.

bioRxiv · Jun 4, 2023Bioengineering

Automated model discovery for muscle using constitutive recurrent neural networks

L. M. Wang, K. Linka, E. Kuhl

This paper introduces a constitutive recurrent neural network to model viscoelastic behavior of skeletal muscle, outperforming traditional models and vanilla neural networks in prediction accuracy.

bioRxiv · May 9, 2023Bioengineering

Automated model discovery for human brain using Constitutive Artificial Neural Networks

K. Linka, S. R. St. Pierre, E. Kuhl

This paper introduces Constitutive Artificial Neural Networks to automatically discover both constitutive models and their parameters for human brain tissue from experimental data.

Acta Biomaterialia · Apr 1, 2023Peer-reviewedBioengineering

Principal-stretch-based constitutive neural networks autonomously discover a subclass of Ogden models for human brain tissue

S. R. St. Pierre, K. Linka, E. Kuhl

This paper introduces a neural network that automatically discovers constitutive models for human brain tissue, outperforming traditional models in capturing tension, compression, and shear behavior.

bioRxiv · Jan 14, 2023Biophysics

Automated model discovery for skin:Discovering the best model, data, and experiment

K. Linka, A. Buganza Tepole, G. A. Holzapfel, E. Kuhl

This paper introduces Constitutive Artificial Neural Networks (CANN) to automatically discover the best constitutive model, parameters, and experiments for skin mechanics.

bioRxiv · Dec 19, 2022Bioengineering