A Methodology to Identify Physical or Computational Experiment Conditions for Uncertainty Mitigation
<|reference_start|>A Methodology to Identify Physical or Computational Experiment Conditions for Uncertainty Mitigation: Complex engineering systems require integration of simulation of sub-systems and calculation of metrics to drive design decisions. This paper introduces a methodology for designing computational or physical experiments for system-level uncertainty mitigation purposes. The methodology follows a previously determined problem ontology, where physical, functional and modeling architectures are decided upon. By carrying out sensitivity analysis techniques utilizing system-level tools, critical epistemic uncertainties can be identified. Afterwards, a framework is introduced to design specific computational and physical experimentation for generating new knowledge about parameters, and for uncertainty mitigation. The methodology is demonstrated through a case study on an early-stage design Blended-Wing-Body (BWB) aircraft concept, showcasing how aerostructures analyses can be leveraged for mitigating system-level uncertainty, by computer experiments or guiding physical experimentation. The proposed methodology is versatile enough to tackle uncertainty management across various design challenges, highlighting the potential for more risk-informed design processes.<|reference_end|>
@article{yarbasi2024a,
    title={A Methodology to Identify Physical or Computational Experiment
  Conditions for Uncertainty Mitigation},
    author={Efe Y. Yarbasi and Dimitri N. Mavris},
    journal={arXiv preprint arXiv:2405.13931},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.13931},
    primaryClass={cs.CE cs.LG}
}