1,806 research outputs found
mfEGRA: Multifidelity Efficient Global Reliability Analysis through Active Learning for Failure Boundary Location
This paper develops mfEGRA, a multifidelity active learning method using
data-driven adaptively refined surrogates for failure boundary location in
reliability analysis. This work addresses the issue of prohibitive cost of
reliability analysis using Monte Carlo sampling for expensive-to-evaluate
high-fidelity models by using cheaper-to-evaluate approximations of the
high-fidelity model. The method builds on the Efficient Global Reliability
Analysis (EGRA) method, which is a surrogate-based method that uses adaptive
sampling for refining Gaussian process surrogates for failure boundary location
using a single-fidelity model. Our method introduces a two-stage adaptive
sampling criterion that uses a multifidelity Gaussian process surrogate to
leverage multiple information sources with different fidelities. The method
combines expected feasibility criterion from EGRA with one-step lookahead
information gain to refine the surrogate around the failure boundary. The
computational savings from mfEGRA depends on the discrepancy between the
different models, and the relative cost of evaluating the different models as
compared to the high-fidelity model. We show that accurate estimation of
reliability using mfEGRA leads to computational savings of 46% for an
analytic multimodal test problem and 24% for a three-dimensional acoustic horn
problem, when compared to single-fidelity EGRA. We also show the effect of
using a priori drawn Monte Carlo samples in the implementation for the acoustic
horn problem, where mfEGRA leads to computational savings of 45% for the
three-dimensional case and 48% for a rarer event four-dimensional case as
compared to single-fidelity EGRA
Multifidelity Active Learning for Failure Estimation of TRISO Nuclear Fuel
The Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear
fuel proposed to be used for multiple modern nuclear technologies. Therefore,
characterizing its safety is vital for the reliable operation of nuclear
technologies. However, the TRISO fuel failure probabilities are small and the
computational model is time consuming to evaluate them using traditional Monte
Carlo-type approaches. In the paper, we present a multifidelity active learning
approach to efficiently estimate small failure probabilities given an expensive
computational model. Active learning suggests the next best training set for
optimal subsequent predictive performance and multifidelity modeling uses
cheaper low-fidelity models to approximate the high-fidelity model output.
After presenting the multifidelity active learning approach, we apply it to
efficiently predict TRISO failure probability and make comparisons to the
reference results
Optimal Model Management for Multifidelity Monte Carlo Estimation
This work presents an optimal model management strategy that exploits multifidelity surrogate models to accelerate the estimation of statistics of outputs of computationally expensive high-fidelity models. Existing acceleration methods typically exploit a multilevel hierarchy of surrogate models that follow a known rate of error decay and computational costs; however, a general collection of surrogate models, which may include projection-based reduced models, data-fit models, support vector machines, and simplified-physics models, does not necessarily give rise to such a hierarchy. Our multifidelity approach provides a framework to combine an arbitrary number of surrogate models of any type. Instead of relying on error and cost rates, an optimization problem balances the number of model evaluations across the high-fidelity and surrogate models with respect to error and costs. We show that a unique analytic solution of the model management optimization problem exists under mild conditions on the models. Our multifidelity method makes occasional recourse to the high-fidelity model; in doing so it provides an unbiased estimator of the statistics of the high-fidelity model, even in the absence of error bounds and error estimators for the surrogate models. Numerical experiments with linear and nonlinear examples show that speedups by orders of magnitude are obtained compared to Monte Carlo estimation that invokes a single model only
Scalable Environment for Quantification of Uncertainty and Optimization in Industrial Applications (SEQUOIA)
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143027/1/6.2017-1327.pd
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