Uncertainty quantification in coupled wildfire-atmosphere simulations at scale.

ABSTRACT: Uncertainties in wildfire simulations pose a major challenge for making decisions about fire management, mitigation, and evacuations. However, ensemble calculations to quantify uncertainties are prohibitively expensive with high-fidelity models that are needed to capture today's ever-more intense and severe wildfires. This work shows that surrogate models trained on related data enable scaling multifidelity uncertainty quantification to high-fidelity wildfire simulations of unprecedented scale with billions of degrees of freedom. The key insight is that correlation is all that matters while bias is irrelevant for speeding up uncertainty quantification when surrogate models are combined with high-fidelity models in multifidelity approaches. This allows the surrogate models to be trained on

SUBMITTER: Schwerdtner P 

PROVIDER: S-EPMC11660924 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature