Basic Framework and Main Methods of Uncertainty Quantification

Zhang, Juan; Ju, Youlun; Wang, Ruili

doi:10.1155/2020/6068203

Cited by 26 publications

(24 citation statements)

References 112 publications

(131 reference statements)

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“…This surrogate data-based ML modeling reduces the computational expense of Monte Carlo methods, which are traditionally used for uncertainty quantification (UQ). 98 Because of uncertainty in process inputs and process states in a chemical process model, the uncertainty propagates to the process outputs as well. Duong et al 99 uses UQ for process design and sensitivity analysis of complex chemical processes using the polynomial chaos theory.…”

Section: An Application Of Reduced-order Modeling To Polymer Manufact...mentioning

confidence: 99%

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A hybrid science‐guided machine learning approach for modeling chemical processes: A review

Sharma

Liu

2022

AIChE Journal

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This study presents a broad perspective of hybrid process modeling combining the scientific knowledge and data analytics in bioprocessing and chemical engineering with a science-guided machine learning (SGML) approach. We divide the approach into two major categories: ML complements science, and science complements ML. We review the literature relating to the hybrid SGML approach, and propose a systematic classification of hybrid SGML models. For applying ML to improve science-based models, we present expositions of direct serial and parallel hybrid modeling and their combinations, inverse modeling, reduced-order modeling, quantifying uncertainty in the process and even discovering governing equations of the process model. For applying scientific principles to improve ML models, we discuss the science-guided design, learning and refinement. For each subcategory, we identify its requirements, strengths, and limitations, together with their published and potential applications. We also present several examples to illustrate different hybrid SGML methodologies for modeling chemical processes.

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Section: An Application Of Reduced-order Modeling To Polymer Manufact...mentioning

confidence: 99%

“…This surrogate data‐based ML modeling reduces the computational expense of Monte Carlo methods, which are traditionally used for uncertainty quantification (UQ) 98 …”

Section: Complements Sciencementioning

confidence: 99%

A hybrid science‐guided machine learning approach for modeling chemical processes: A review

Sharma

Liu

2022

AIChE Journal

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“…, θ M }. The principal idea of BI consists of computing the probabilistic distribution p(θ|y) of the random vector θ conditional on training data y using the Bayes' theorem [17,18]:…”

Section: Classical Bayesian Inferencementioning

confidence: 99%

Time-Dependent Behavior of Callovo-Oxfordian Claystone for Nuclear Waste Disposal: Uncertainty Quantification from In-Situ Convergence Measurements

Tran²,

Hoxha

et al. 2022

Sustainability

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The sustainability of geotechnical infrastructures is closely linked with their long-time behavior. In fact, there is not a straightforward procedure to predict this behavior, and very often, the back analyses of observed data are the best tool to understand their long-time response. In-situ observations of drifts constructed in the Callovo-Oxfordian (COx) claystone, the potential host formation for geological radioactive waste disposal, in France exhibit a progressive convergence. These convergence measurements with quite significant dispersions reveal a considerable uncertainty of time-dependent behavior of this argillaceous rock that can strongly affect the transmit loading to liners, hence the long term stability of the drift. Consequently, the uncertain quantification of the creep behavior of COx claystone presents an important task before analyzing the safety of the waste disposal system. In this work, this challenge was conducted by using the well-known Bayesian inference technique. For this aim, on the one hand, the effectiveness of the classical and hierarchical Bayesian techniques to quantify the epistemic and aleatoric uncertainties of the time-dependent behavior of the host rock were investigated using synthetic data. On the other hand, we dealt with the uncertain quantification of the Lemaitre parameters that characterize the visco-plastic behavior of COx claystone thanks to the real data of in-situ convergence measurements of drifts.

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“…Quantifying these uncertainties is an essential step in designing reliable and efficient systems that involve testing, modelling, and simulation; this is known as uncertainty quantification (UQ). UQ allows to make better design decisions, reduce the cost of trial and error during code development, and improve the reliability of simulation through identifying the primary source of uncertainty, analysing how the uncertainty propagates, and searching for stable, optimised solutions [35]. The general procedure and processes which form the UQ are illustrated in Fig.…”

Section: Adhesive Joint Defects and Uncertaintiesmentioning

confidence: 99%

“…Fig. 6 General uncertainty quantification framework and main process from [35] Fig. 7 Categories, classification, and correlation of common defects and uncertainties within adhesively bonded joints…”

Section: Kissing Bondmentioning

confidence: 99%

Defects and uncertainties of adhesively bonded composite joints

2021

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The increasing use of fibre reinforced polymer composite materials in a wide range of applications increases the use of similar and dissimilar joints. Traditional joining methods such as welding, mechanical fastening and riveting are challenging in composites due to their material properties, heterogeneous nature, and layup configuration. Adhesive bonding allows flexibility in materials selection and offers improved production efficiency from product design and manufacture to final assembly, enabling cost reduction. However, the performance of adhesively bonded composite structures cannot be fully verified by inspection and testing due to the unforeseen nature of defects and manufacturing uncertainties presented in this joining method. These uncertainties can manifest as kissing bonds, porosity and voids in the adhesive. As a result, the use of adhesively bonded joints is often constrained by conservative certification requirements, limiting the potential of composite materials in weight reduction, cost-saving, and performance. There is a need to identify these uncertainties and understand their effect when designing these adhesively bonded joints. This article aims to report and categorise these uncertainties, offering the reader a reliable and inclusive source to conduct further research, such as the development of probabilistic reliability-based design optimisation, sensitivity analysis, defect detection methods and process development.

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Basic Framework and Main Methods of Uncertainty Quantification

Cited by 26 publications

References 112 publications

A hybrid science‐guided machine learning approach for modeling chemical processes: A review

A hybrid science‐guided machine learning approach for modeling chemical processes: A review

Time-Dependent Behavior of Callovo-Oxfordian Claystone for Nuclear Waste Disposal: Uncertainty Quantification from In-Situ Convergence Measurements

Defects and uncertainties of adhesively bonded composite joints

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