Scalable physics-informed deep generative model for solving forward and inverse stochastic differential equations.
- DOI
- 10.1016/j.neunet.2026.108770
- Published
- 2026 Aug
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.108770,
title = {Scalable physics-informed deep generative model for solving forward and inverse stochastic differential equations.},
author = {Zhou S and You W and Guo L and Meng X},
year = {2026},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2026.108770},
url = {https://doi.org/10.1016/j.neunet.2026.108770}
}RIS
TY - JOUR TI - Scalable physics-informed deep generative model for solving forward and inverse stochastic differential equations. AU - Zhou S AU - You W AU - Guo L AU - Meng X PY - 2026 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2026.108770 UR - https://doi.org/10.1016/j.neunet.2026.108770 ER -
APA
S, Z., W, Y., L, G., & X, M. (2026). Scalable physics-informed deep generative model for solving forward and inverse stochastic differential equations.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2026.108770
Source records
- pubmed · retrieved 2026-09-26T09:44:28.010Z
- europe-pmc · retrieved 2026-09-26T09:44:28.019Z