Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution
- DOI
- 10.1007/s10596-024-10313-x
- Published
- 2024-08-14
- Container
- Computational Geosciences
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s10596-024-10313-x,
title = {Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution},
author = {Sarah Perez and Philippe Poncet},
year = {2024},
journal = {Computational Geosciences},
doi = {10.1007/s10596-024-10313-x},
url = {https://doi.org/10.1007/s10596-024-10313-x}
}RIS
TY - JOUR TI - Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution AU - Sarah Perez AU - Philippe Poncet PY - 2024 JO - Computational Geosciences DO - 10.1007/s10596-024-10313-x UR - https://doi.org/10.1007/s10596-024-10313-x ER -
APA
Perez, S., & Poncet, P. (2024). Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution. Computational Geosciences. https://doi.org/10.1007/s10596-024-10313-x
Source records
- crossref · retrieved 2026-11-06T21:42:23.505Z