Modeling, analysis, and optimization under uncertainties: a review

Acar, Erdem; Bayrak, Gamze; Jung, Yongsu; Lee, Ikjin; Ramu, Palaniappan; Ravichandran, S.

doi:10.1007/s00158-021-03026-7

Cited by 63 publications

(17 citation statements)

References 407 publications

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“…Based on the above discussions of different UQ methods as well as their pros and cons, we give some remarks regarding the choice of the approaches when one attempts to carry out the uncertainty analysis of rotor systems. The most important supports for choosing a UQ method are the details available and the computational budget [24]. The type of data available will affect the most appropriate method one should choose.…”

Section: Extract Boundsmentioning

confidence: 99%

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A state-of-the-art review on uncertainty analysis of rotor systems

Sinou

Zhu

et al. 2023

Mechanical Systems and Signal Processing

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Section: Extract Boundsmentioning

confidence: 99%

“…In different uncertainty domains, various UQ approaches [18][19][20], as well as solutions to improve efficiency and accuracy, have been proposed [21][22][23]. Not all procedures are available in specific situations and principles exist for selection [24].…”

Section: Introductionmentioning

confidence: 99%

A state-of-the-art review on uncertainty analysis of rotor systems

Sinou

Zhu

et al. 2023

Mechanical Systems and Signal Processing

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“…Whatever the prediction process followed, every step may lead to uncertainties, and the decision‐making should consider these uncertainties. It is good practice to directly evaluate these uncertainties in the modeling phase (Acar et al, 2021; Peng & Zhao, 2009). However this can prove a difficult task, especially when elementary models from different scientific branches are linked together (e.g., microbiology with heat and mass transfer in food processes) and the propagation pathways become complex (Kirchner et al, 2021).…”

Section: Future Trendsmentioning

confidence: 99%

“…Whatever the prediction process followed, every step may lead to uncertainties, and the decision-making should consider these uncertainties. It is good practice to directly evaluate these uncertainties in the modeling phase (Acar et al, 2021;Peng & Zhao, 2009).…”

Section: Future Trendsmentioning

confidence: 99%

A primer on predictive techniques for food and bioresources transformation processes

Sicard

Barbe²,

Boutrou

et al. 2023

J Food Process Engineering

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To meet current societal demand for more sustainable transformation processes and bioresources, these processes must be optimized and new ones developed. The evolution of various systems (raw material, food, or process attributes) can be predicted to optimize the uses of biomass for better quality, safety, economic benefit, and sustainability. Predictive modeling can guide the necessary changes and influence industrials, governmental policies and consumers decision-making. However, achieving good predictive capability requires reflection on the models and model validation, which can be difficult. This review aims to help scientists begin to predict by presenting the techniques currently used in predictive science for food and related bioproducts. First, a guideline helps readers initiate a prediction process along with final tips and a warning about the risks involved, with a particular focus on the crucial validation step. Three broad categories of techniques are then presented: empirical, mechanistic, and artificial intelligence (or "data-driven"). For each category, the advantages and limitations of current techniques for prediction are explained in light of their current domains of applications, illustrated with literature studies and a detailed example. Thus this article provides engineering researchers information about predictive modeling which is a recent relevant development in optimization of both food and nonfood bioresources processes. Practical applicationsPredictive modeling is a recent development of much relevance in the optimization of both food and nonfood bioresources processes. The goal of this article is to guide those in research or industry who would like to start predicting. Therefore, the article is intended as a primer on prediction concepts and predictive techniques for food and non-food bioresources processing. Three categories of techniques commonly used in these fields are illustrated by various examples of current applications and a more detailed example helps to understand the implementation process. An increased ability of the global scientific body to predict the outcome of various decisions, often linked or sequential, will open new avenues for designing food products with circularity in mind: maintaining value and not creating waste in the process.

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“…natural variability) cannot be eliminated, even if epistemic uncertainty (a.k.a. informative uncertainty) is eliminated [32]. Moreover, data may also involve epistemic uncertainty stemming from imprecise observations and incomplete knowledge in physical experiments, among other sources.…”

Section: Introductionmentioning

confidence: 99%

Computation‐with‐confidence for static elasticity: Data‐driven approach with order statistics

Kanno

2023

Z Angew Math Mech

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This paper presents a data‐driven method to identify a bound for the structural response of interest when a data set of elastic material responses is available. The obtained bound is probable in the sense that, at least with a certain confidence level, the probability that the structural response is within the bound is no smaller than a certain target value of reliability. The confidence level is ensured by considering the order statistics of the given data points. In this manner, this paper introduces the concept of reliability to the framework of data‐driven computational elasticity to address uncertainty in the data set. The method does not require any statistical information of the data points. Two numerical examples demonstrate the use of the proposed method to analyze static responses of structures by using only given material data sets.

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Modeling, analysis, and optimization under uncertainties: a review

Cited by 63 publications

References 407 publications

A state-of-the-art review on uncertainty analysis of rotor systems

A state-of-the-art review on uncertainty analysis of rotor systems

A primer on predictive techniques for food and bioresources transformation processes

Computation‐with‐confidence for static elasticity: Data‐driven approach with order statistics

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