The Physics-guided Mixture Density Networks (PgMDN) is proposed for uncertainty quantification for complex problems with sparse data. Here, the complexity ...
Abstract: This paper proposes a Physics-guided Mixture Density Network (PgMDN) model for uncertainty quantification of regression-type analysis.
This paper proposes a Physics-guided Mixture Density Network (PgMDN) model for uncertainty quantification of regression-type analysis.
This paper proposes a Physics-guided Mixture Density Network (PgMDN) model for uncertainty quantification of regression-type analysis.
Physics-guided mixture density networks for uncertainty quantification
ideas.repec.org › eee › reensy
This paper proposes a Physics-guided Mixture Density Network (PgMDN) model for uncertainty quantification of regression-type analysis.
People also ask
What are mixture density networks?
What is uncertainty quantification in physics?
Physics-guided Mixture Density Networks for Uncertainty Quantification .....123 ... 3.4 Physics-guided Mixture Density Networks for Uncertainty Quantification.
Oct 9, 2023 · A Physics-guided Mixture Density Network (PgMDN) model is proposed for uncertainty quantification of fatigue data analysis in this paper.
A Physics-guided Mixture Density Network (PgMDN) model is proposed for uncertainty quantification of fatigue data analysis in this paper.
Jun 19, 2023 · A Physics-guided Mixture Density Network (PgMDN) model is proposed for uncertainty quantification of fatigue data analysis in this paper. It ...
Physics-guided mixture density networks for uncertainty quantification. J Chen, Y Yu, Y Liu. Reliability Engineering & System Safety 228, 108823, 2022. 8, 2022.