High-speed predictions of post-burn contraction using a neural network trained on 2D-finite element simulations
- DOI
- 10.3389/fams.2023.1098242
- Published
- 2023-01-30
- Container
- Frontiers in Applied Mathematics and Statistics
- Publisher
- Frontiers Media SA
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3389/fams.2023.1098242,
title = {High-speed predictions of post-burn contraction using a neural network trained on 2D-finite element simulations},
author = {Ginger Egberts and Fred Vermolen and Paul van Zuijlen},
year = {2023},
journal = {Frontiers in Applied Mathematics and Statistics},
doi = {10.3389/fams.2023.1098242},
url = {https://doi.org/10.3389/fams.2023.1098242}
}RIS
TY - JOUR TI - High-speed predictions of post-burn contraction using a neural network trained on 2D-finite element simulations AU - Ginger Egberts AU - Fred Vermolen AU - Paul van Zuijlen PY - 2023 JO - Frontiers in Applied Mathematics and Statistics DO - 10.3389/fams.2023.1098242 UR - https://doi.org/10.3389/fams.2023.1098242 ER -
APA
Egberts, G., Vermolen, F., & Zuijlen, P. V. (2023). High-speed predictions of post-burn contraction using a neural network trained on 2D-finite element simulations. Frontiers in Applied Mathematics and Statistics. https://doi.org/10.3389/fams.2023.1098242
Source records
- crossref · retrieved 2026-09-25T14:40:24.631Z