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Jiawei Yong, Sihai Tang • Entropy
(2026)
Solving Bayesian inverse problems efficiently stands as a major bottleneck in scientific computing. Although Bayesian Physics-Informed Neural Networks (B-PINNs) have introduced a robust way to quantify uncertainty, the high-dimensional para...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/e28020156
Peter R. Wiecha, Arnaud Arbouet, Christian Girard, Otto L. Muskens • Photonics Research
(2021)
Deep learning in the context of nano-photonics is mostly discussed in terms of its potential for inverse design of photonic devices or nano-structures. Many of the recent works on machine-learning inverse design are highly specific, and the...
OPENALEX
Open Access
DOI: https://doi.org/10.1364/prj.415960