MOOSE ProbML: Parallelized Probabilistic Machine Learning and Uncertainty Quantification for Computational Energy Applications
- Idaho National Laboratory
- Georgia Institute of Technology
- Arizona State University
This paper presents the development and demonstration of massively parallel probabilistic machine learning (ML) and uncertainty quantification (UQ) capabilities within the Multiphysics Object-Oriented Simulation Environment (MOOSE), an open-source computational platform for parallel finite element and finite volume analyses. In addressing the computational expense and uncertainties inherent in complex multiphysics simulations, this paper tackles the integration of Gaussian process (GP) variants, active learning, Bayesian inverse UQ, adaptive forward UQ, Bayesian optimization, evolutionary optimization, and Markov chain Monte Carlo (MCMC) within MOOSE. It also elaborates on the interaction among key MOOSE systems---\texttt{Sampler}, \texttt{MultiApp}, \texttt{Reporter}, and \texttt{Surrogate}---in enabling these capabilities. The modularity offered by these systems enables development of a multitude of probabilistic ML and UQ algorithms in MOOSE. Example code demonstrations include parallel active learning and parallel Bayesian inference via active learning. The impact of these developments is illustrated through five applications relevant to computational energy applications: UQ of nuclear fuel fission product release, using parallel active learning Bayesian inference; nuclear microreactor very rare events analysis, using active learning; advanced manufacturing process modeling, using multi-output GPs (MOGPs) and dimensionality reduction; fluid flow using deep GPs (DGPs); and tritium transport model parameter optimization for fusion energy, using batch Bayesian optimization.
- Research Organization:
- Idaho National Laboratory (INL), Idaho Falls, ID (United States)
- Sponsoring Organization:
- USDOE Office of Nuclear Energy (NE); USDOE Office of Nuclear Energy (NE)
- Grant/Contract Number:
- AC07-05ID14517
- OSTI ID:
- 3012815
- Report Number(s):
- INL/JOU-25-83717
- Journal Information:
- Journal of Computational Science, Journal Name: Journal of Computational Science Journal Issue: 0 Vol. 94
- Country of Publication:
- United States
- Language:
- English
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