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Propagation of uncertainties and applications in numerical modeling: tutorial.
Summary
This study presents methods for propagating experimental uncertainties through computational models. These techniques aid in metrology, optimization, and model comparison, using light scattering by gold nanoparticles as a case study.
Area of Science:
- Computational Physics
- Nanophotonics
- Metrology
Background:
- Computational model inputs often come from external sources, introducing uncertainties.
- Experimental uncertainty propagation is crucial for metrology and understanding physical systems.
- Characterizing and comparing models benefits from uncertainty information.
Purpose of the Study:
- To provide tools and applications for propagating experimental uncertainties through computational models.
- To illustrate uncertainty propagation using light scattering by gold nanoparticles.
- To compare the full Mie theory and dipole approximation for nanoparticle scattering.
Main Methods:
- Propagation of experimental input uncertainties through numerical models.
- Analysis of output samples generated from propagated uncertainties.
- Application to the scattering of light by gold nanoparticles.
Main Results:
- Demonstration of uncertainty propagation tools and their applications.
- Comparison of Mie theory and dipole approximation for gold nanoparticles.
- Specific study of localized surface plasmon resonance position and scattering efficiency.
Conclusions:
- Uncertainty propagation offers valuable insights into model behavior and physical systems.
- The presented methods are applicable to various scientific and engineering domains.
- Light scattering by nanoparticles serves as an effective model system for demonstrating these techniques.
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