Global Sensitivity Analysis Using Polynomial Chaos Expansion on the Grassmann Manifold

Bazyleva, Valentina; Garibay, Victoria M.; Roy, D.

doi:10.1007/978-3-031-36030-5_46

Cited by 1 publication

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, we describe the standard approaches for computing Sobol' indices as well as the proposed GSA framework, built upon our previous work 12 ; the primary improvement is the use of sparse PCE, specifically, least angle regression (LAR), to calculate PCE coefficients.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Trajectory-based global sensitivity analysis in multiscale models

Bazyleva,

Garibay,

Roy

2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

This research introduces a novel global sensitivity analysis (GSA) framework for agent-based models (ABMs) that explicitly handles their distinctive features, such as multi-level structure and temporal dynamics. The framework uses Grassmannian diffusion maps to reduce output data dimensionality and sparse polynomial chaos expansion (PCE) to compute sensitivity indices for stochastic input parameters. To demonstrate the versatility of the proposed GSA method, we applied it to a non-linear system dynamics model and epidemiological and economic ABMs, depicting different dynamics. Unlike traditional GSA approaches, the proposed method enables a more general estimation of parametric sensitivities spanning from the micro level (individual agents) to the macro level (entire population). The new framework encourages the use of manifold-based techniques in uncertainty quantification, enhances understanding of complex spatio-temporal processes, and equips ABM practitioners with robust tools for detailed model analysis. This empowers them to make more informed decisions when developing, fine-tuning, and verifying models, thereby advancing the field and improving routine practice for GSA in ABMs.

show abstract

Section: Methodsmentioning

confidence: 99%

“…https://doi.org/10.1038/s41598-024-64331-x www.nature.com/scientificreports/ GSA using a manifold learning-based PCE based on12 .…”

mentioning

confidence: 99%