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Learning and Optimization with Bayesian Hybrid Models

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Elvis A. Eugene, Xian Gao, Alexander W. Dowling

Optimization and Control · Submitted 12 Dec 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Ultimately, we argue Bayesian hybrid models are an emerging paradigm for data-informed decision-making under parametric and epistemic uncertainty.

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arXiv ID1912.06269 (version 1)
Pith IDJHI3CNRE, Pith's permanent record of this paper and its review
CitationAlexander W. Dowling, Elvis A. Eugene, Xian Gao. "Learning and Optimization with Bayesian Hybrid Models." arXiv:1912.06269 (2019). https://pith.science/paper/JHI3CNRE
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,386 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.12 · human participants 0.02 · controlled comparison 0.81 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.91 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Bayesian hybrid models fuse physics-based insights with machine learning constructs to correct for systematic bias. In this paper, we compare Bayesian hybrid models against physics-based glass-box and Gaussian process black-box surrogate models. We consider ballistic firing as an illustrative case study for a Bayesian decision-making workflow. First, Bayesian calibration is performed to estimate model parameters. We then use the posterior distribution from Bayesian analysis to compute optimal firing conditions to hit a target via a single-stage stochastic program. The case study demonstrates the ability of Bayesian hybrid models to overcome systematic bias from missing physics with less data than the pure machine learning approach. Ultimately, we argue Bayesian hybrid models are an emerging paradigm for data-informed decision-making under parametric and epistemic uncertainty.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Bayesian hybrid models fuse physics-based insights with machine learning constructs to correct for systematic bias. background 0.96 0.10 0.08 asserted 1.00
s1 In this paper, we compare Bayesian hybrid models against physics-based glass-box and Gaussian process black-box surrogate models. contribution 0.76 0.79 0.03 asserted 0.72
s2 We consider ballistic firing as an illustrative case study for a Bayesian decision-making workflow. method 0.59 0.76 0.02 asserted 0.93
s3 First, Bayesian calibration is performed to estimate model parameters. method 1.00 0.84 0.02 asserted 0.96
s4 We then use the posterior distribution from Bayesian analysis to compute optimal firing conditions to hit a target via a single-stage stochastic program. method 0.99 0.92 0.06 asserted 0.98
s5 The case study demonstrates the ability of Bayesian hybrid models to overcome systematic bias from missing physics with less data than the pure machine learning approach. result 0.86 0.91 0.34 asserted 1.00
s6 Ultimately, we argue Bayesian hybrid models are an emerging paradigm for data-informed decision-making under parametric and epistemic uncertainty. interpretation 0.99 0.22 0.86 asserted 0.56

What kind of paper the classifier reports this is: simulation study 0.48 · evidence from simulation or computation 0.99.

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  • C citations
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Bayesian hybrid models fuse physics-based insights with machine learning constructs to correct for systematic bias. In this paper, we compare Bayesian hybrid models against physics-based glass-box and Gaussian process black-box surrogate models. We consider ballistic firing as an illustrative case study for a Bayesian decision-making workflow. First, Bayesian calibration is performed to estimate model parameters. We then use the posterior distribution from Bayesian analysis to compute optimal firing conditions to hit a target via a single-stage stochastic program. The case study demonstrates the ability of Bayesian hybrid models to overcome systematic bias from missing physics with less data than the pure machine learning approach. Ultimately, we argue Bayesian hybrid models are an emerging paradigm for data-informed decision-making under parametric and epistemic uncertainty.

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