A methodology to assess and improve the physics consistency of an artificial neural network regression model for engineering applications
- DOI
- 10.1186/s40323-022-00224-w
- Published
- 7
- Container
- Advanced Modeling and Simulation in Engineering Sciences
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1186/s40323-022-00224-w,
title = {A methodology to assess and improve the physics consistency of an artificial neural network regression model for engineering applications},
author = {E. Rajasekhar Nicodemus},
year = {2022},
journal = {Advanced Modeling and Simulation in Engineering Sciences},
doi = {10.1186/s40323-022-00224-w},
url = {https://doi.org/10.1186/s40323-022-00224-w}
}RIS
TY - JOUR TI - A methodology to assess and improve the physics consistency of an artificial neural network regression model for engineering applications AU - E. Rajasekhar Nicodemus PY - 2022 JO - Advanced Modeling and Simulation in Engineering Sciences DO - 10.1186/s40323-022-00224-w UR - https://doi.org/10.1186/s40323-022-00224-w ER -
APA
Nicodemus, E. R. (2022). A methodology to assess and improve the physics consistency of an artificial neural network regression model for engineering applications. Advanced Modeling and Simulation in Engineering Sciences. https://doi.org/10.1186/s40323-022-00224-w