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Found 6 papers, 0 papers with code
A Methodology to Identify Physical or Computational Experiment Conditions for Uncertainty Mitigation
This paper introduces a methodology for designing computational or physical experiments for system-level uncertainty mitigation purposes.
A Multi-Fidelity Methodology for Reduced Order Models with High-Dimensional Inputs
In the early stages of aerospace design, reduced order models (ROMs) are crucial for minimizing computational costs associated with using physics-rich field information in many-query scenarios requiring multiple evaluations.
Improved Aircraft Environmental Impact Segmentation via Metric Learning
Accurate modeling of aircraft environmental impact is pivotal to the design of operational procedures and policies to mitigate negative aviation environmental impact.
Decentralized and Privacy-Preserving Learning of Approximate Stackelberg Solutions in Energy Trading Games with Demand Response Aggregators
In this work, a novel Stackelberg game theoretic framework is proposed for trading energy bidirectionally between the demand-response (DR) aggregator and the prosumers.
Multi-Class Multiple Instance Learning for Predicting Precursors to Aviation Safety Events
Multiple binary classifiers are also capable of determining the aircraft's parameters that are correlated to these events.
Aerodynamic Data Fusion Towards the Digital Twin Paradigm
For example, two sources of pressure fields about an aircraft are fused based on measured forces and moments from a wind-tunnel experiment.
