Uncertainty Quantification in Mathematics-Embedded Ontologies Using Stochastic Reduced Order Model
Abstract
References
Index Terms
- Uncertainty Quantification in Mathematics-Embedded Ontologies Using Stochastic Reduced Order Model
Recommendations
Numerical approach for quantification of epistemic uncertainty
In the field of uncertainty quantification, uncertainty in the governing equations may assume two forms: aleatory uncertainty and epistemic uncertainty. Aleatory uncertainty can be characterised by known probability distributions whilst epistemic ...
Quantification of model-form and predictive uncertainty for multi-physics simulation
Traditional uncertainty quantification in multi-physics design problems involves the propagation of parametric uncertainties in input variables such as structural or aerodynamic properties through a single, or series of models constructed to represent ...
Uncertainty quantification methods for evolutionary optimization under uncertainty
GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionIn this paper, we discuss the role of uncertainty quantification (UQ) in assisting optimization under uncertainty. UQ plays a significant role in quantifying the robustness of solutions so as to help the optimizer in achieving robust optimum solutions. ...
Comments
Information & Contributors
Information
Published In
Publisher
IEEE Educational Activities Department
United States
Publication History
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in