Physics-informed learning under epistemic uncertainty with an application to system health modeling

DOI10.1016/j.ijar.2023.108988MaRDI QIDQ6178727

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Publication date: 4 September 2023

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

zbMATH Keywords

maximum likelihoodepistemic uncertaintyremaining useful lifephysics-informed learning

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37)

Cites Work

• Frequency domain identification of continuous-time output error models. I: Uniformly sampled data and frequency function approximation
• Identification of elastic properties in the belief function framework
• Probability-possibility transformations, triangular fuzzy sets, and probabilistic inequalities
• A min-max regret approach to maximum likelihood inference under incomplete data
• Statistical reasoning with set-valued information: ontic vs. epistemic views
• Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons
• THE NECESSITY OF THE STRONG α-CUTS OF A FUZZY SET
• DEALING WITH INCONSISTENT MEASUREMENTS IN INVERSE PROBLEMS: SET-BASED APPROACH

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