A minimalistic approach to physics-informed machine learning using neighbour lists as physics-optimized convolutions for inverse problems involving particle systems
- DOI
- 10.1016/j.jcp.2022.111750
- Published
- 2023-01
- Container
- Journal of Computational Physics
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.jcp.2022.111750,
title = {A minimalistic approach to physics-informed machine learning using neighbour lists as physics-optimized convolutions for inverse problems involving particle systems},
author = {Alessio Alexiadis},
year = {2023},
journal = {Journal of Computational Physics},
doi = {10.1016/j.jcp.2022.111750},
url = {https://doi.org/10.1016/j.jcp.2022.111750}
}RIS
TY - JOUR TI - A minimalistic approach to physics-informed machine learning using neighbour lists as physics-optimized convolutions for inverse problems involving particle systems AU - Alessio Alexiadis PY - 2023 JO - Journal of Computational Physics DO - 10.1016/j.jcp.2022.111750 UR - https://doi.org/10.1016/j.jcp.2022.111750 ER -
APA
Alexiadis, A. (2023). A minimalistic approach to physics-informed machine learning using neighbour lists as physics-optimized convolutions for inverse problems involving particle systems. Journal of Computational Physics. https://doi.org/10.1016/j.jcp.2022.111750
Source records
- crossref · retrieved 2026-09-25T12:31:05.635Z