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
integrates 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 — Sampler, MultiApp, Reporter, and
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; very rare events analysis in
nuclear microreactors 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.

Este artículo explora los viajes en el tiempo y sus implicaciones.

Descargar PDF: