exauq
EXAscale Uncertainty Quantification Toolbox (EXAUQ-Toolbox)
The exauq package provides a comprehensive suite of tools for developing Gaussian Process (GP)
emulators to model complex computer simulations. A core feature is its support for multi-level
GP emulation, enabling efficient surrogate modeling of simulation hierarchies with varying levels
of fidelity. The highest-fidelity simulations in these hierarchies may require exascale computing
resources, while lower-fidelity approximations can be executed on conventional HPC clusters or
departmental servers.
Beyond emulation, exauq facilitates the management of computational resources for large-scale
simulations. It abstracts job submission, monitoring, and retrieval across both local and remote
hardware environments, streamlining simulation workflows for uncertainty quantification tasks.
Key Features
- Multi-level GP emulation: Train hierarchical Gaussian Process models for multi-fidelity simulations.
- Experimental design: Generate effective sample distributions using Latin hypercube and leave-one-out (LOO) adaptive sampling methods.
- Bounded hyperparameter control: Extends
mogp_emulatorwith hyperparameter bounding capabilities. - Simulation job management: Submit, monitor, and retrieve simulation results across distributed computing environments, including SSH-based remote execution.
- Flexible hardware interfaces: Abstract interactions with local and remote computational resources.
- Offline documentation: Integrated documentation viewer for offline use.
Subpackages
-
core: Implements the mathematical and statistical methods for experimental design, GP emulation, and numerical tolerance checks. -
sim_management: Provides infrastructure for managing and orchestrating simulation jobs across various computing environments.
References
mogp_emulator: https://github.com/alan-turing-institute/mogp-emulator- LOO Sampling:
- Mohammadi, H. et al. (2022) "Cross-Validation-based Adaptive Sampling for Gaussian Process Models". DOI: https://doi.org/10.1137/21M1404260
- Kimpton, L. M. et al. (2023) "Cross-Validation Based Adaptive Sampling for Multi-Level Gaussian Process Models". arXiv: https://arxiv.org/abs/2307.09095
Developed by the Research Software Engineering (RSE) team at the University of Exeter, UK, as part of the ExCALIBUR project (EPSRC grant number EP/W007886/1).