Comparing emulation methods for a high‐resolution storm surge model
- Department of Statistics University of California Santa Cruz Santa Cruz California USA, Statistical Sciences Group Los Alamos National Laboratory Los Alamos New Mexico USA
- Department of Statistics University of California Santa Cruz Santa Cruz California USA
- Statistical Sciences Group Los Alamos National Laboratory Los Alamos New Mexico USA
Abstract Realistic simulations of complex systems are fundamental for climate and environmental studies. Large computer systems are often not sufficient to run sophisticated computational models for large numbers of different input settings. Statistical surrogate models, or emulators, are key tools enabling fast exploration of the simulator input space. Gaussian processes have become standard for computer simulator emulation. However, they require careful implementation to scale appropriately, motivating alternative methods more recently introduced. We present a comparison study of surrogates of the Sea, Lake, and Overland Surges from Hurricanes (SLOSH) simulator—the simulator of choice for government agencies—using four emulation approaches: BASS; BART; SEPIA; and RobustGaSP. SEPIA and RobustGaSP use Gaussian processes, BASS implements adaptive splines, and BART is based on ensembles of regression trees. We describe the four models and compare them in terms of computation time and predictive metrics. These surrogates use proven and distinct methodologies, are available through accessible software, and quantify prediction uncertainty. Our data cover millions of response values. We find that SEPIA and RobustGaSP provide exceptional predictive power, but cannot scale to emulate experiments as large as the one considered in this paper as effectively as BASS and BART.
- Sponsoring Organization:
- USDOE
- OSTI ID:
- 1924561
- Journal Information:
- EnvironMetrics (London), Journal Name: EnvironMetrics (London) Journal Issue: 3 Vol. 34; ISSN 1180-4009
- Publisher:
- Wiley Blackwell (John Wiley & Sons)Copyright Statement
- Country of Publication:
- United Kingdom
- Language:
- English
Similar Records
Sensitivity Analysis and Emulation for Functional Data using Bayesian Adaptive Splines
Designing accurate emulators for scientific processes using calibration-driven deep models