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Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures?
The quantification of aleatoric and epistemic uncertainty in terms of conditional entropy and
mutual information, respectively, has recently become quite common in machine learning …
mutual information, respectively, has recently become quite common in machine learning …
[HTML][HTML] A robust learning methodology for uncertainty-aware scientific machine learning models
Robust learning is an important issue in Scientific Machine Learning (SciML). There are
several works in the literature addressing this topic. However, there is an increasing demand …
several works in the literature addressing this topic. However, there is an increasing demand …
A probabilistic model for aircraft in climb using monotonic functional Gaussian process emulators
Ensuring vertical separation is a key means of maintaining safe separation between aircraft
in congested airspace. Aircraft trajectories are modelled in the presence of significant …
in congested airspace. Aircraft trajectories are modelled in the presence of significant …
GAR: generalized autoregression for multi-fidelity fusion
In many scientific research and engineering applications where repeated simulations of
complex systems are conducted, a surrogate is commonly adopted to quickly estimate the …
complex systems are conducted, a surrogate is commonly adopted to quickly estimate the …
Gar: Generalized autoregression for multi-fidelity fusion
Y Wang, Z **ng, WW **ng - arxiv preprint arxiv:2301.05729, 2023 - arxiv.org
In many scientific research and engineering applications where repeated simulations of
complex systems are conducted, a surrogate is commonly adopted to quickly estimate the …
complex systems are conducted, a surrogate is commonly adopted to quickly estimate the …