Multilevel Monte Carlo applied for uncertainty quantification in stochastic multiscale systems

Kimaev, Grigoriy; Chaffart, Donovan; Ricardez‐Sandoval, Luis A.

doi:10.1002/aic.16262

Cited by 17 publications

(19 citation statements)

References 76 publications

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“…Besides 2D materials, in the context of nanoparticle and nanorod growth, MC simulations [12,40], multiscale modeling [41,42] and enhanced multiscale modeling with Artificial Neural Network (ANN) [43][44][45][46] have been performed. "Classical" kMC models have also used for the deposition of diamond [47], AlN [48] and GaAs [49] film, plasma enhanced a-Si:H CVD [50], hybrid MD/kMC [51] and for growth in extereme pressure conditions [52].…”

Section: Chemical Vapor Depositionmentioning

confidence: 99%

Monte Carlo and Kinetic Monte Carlo Models for Deposition Processes: A Review of Recent Works

Cheimarios

Kokkoris

et al. 2021

Front. Phys.

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Monte Carlo (MC) and kinetic Monte Carlo (kMC) models are widely used for studying the physicochemical surface phenomena encountered in most deposition processes. This spans from physical and chemical vapor deposition to atomic layer and electrochemical deposition. MC and kMC, in comparison to popular molecular methods, such as Molecular Mechanics/Dynamics, have the ability to address much larger time and spatial scales. They also offer a far more detailed approach of the surface processes than continuum-type models, such as the reaction-diffusion models. This work presents a review of the modern applications of MC/kMC models employed in deposition processes.

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Section: Chemical Vapor Depositionmentioning

confidence: 99%

Monte Carlo and Kinetic Monte Carlo Models for Deposition Processes: A Review of Recent Works

Cheimarios

Kokkoris

et al. 2021

Front. Phys.

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“…There are three uncertainty quantification models namely Monte Carlo (MC; Kimaev et al, 2020), the Bootstrap model (Lai, 2020) and the GMM (Shafiullah et al, 2020). Srivastav et al (2013) used the GMM approach that fathoms the issue of assessing the joint probability density of the data under consideration in the maximum-likelihood.…”

Section: Experiments Setupmentioning

confidence: 99%

Meta‐heuristic optimization algorithm for predicting software defects

2021

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Software engineering companies strive to improve software quality by predicting software defects-prone modules. Although various data mining methods have been developed, unstable accuracy rates are still critical issues owing to the imbalanced nature and high dimensionality of software defect datasets. To deal with this issue, we propose a spotted hyena, a novel meta-heuristic optimization algorithm for predicting software defects. Support and confidence in classification rules are the basis of a multi-objective fitness function that assists the spotted hyena algorithm in serving as a classifier by finding the fittest classification or standard rules among individuals. Experiments were conducted on four NASA software datasets, JM1, KC2, KC1, and PC3. The spotted hyena classifier provides an accuracy of 85.2, 84, 89.6, and 81.8%, respectively, for these datasets. These accuracy rates are better than those achieved using other popular data mining techniques. We also discuss other classification measures in connection with the experimental results, such as precision, recall, and confusion matrices, in connection with the experimental results. Moreover, the Gaussian mixture model is used to study the uncertainty quantification of the proposed classifier. The study proved the feasible performance of the spotted hyena classifier in four different case studies.

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“…The study of Bhonsale et al [39] and Makrygiorgos et al [41] show the advantages of using non-intrusive Polynomial Chaos Expansion-based (PCE) algorithms over brute-force Monte Carlo Simulations (MCS) for uncertainty quantification in dynamic models. This PCE algorithm can be enhanced by implementing advanced sampling methods to increase the precision of the UQ analysis [42]. The advanced techniques developed in Paulson et al can be implemented to allow the PCE to capture singularities that can occur during a dynamic process [43].…”

Section: Introductionmentioning

confidence: 99%

How sensitive is a dynamic ammonia synthesis process? Global sensitivity analysis of a dynamic Haber-Bosch process (for flexible seasonal energy storage)

Verleysen

Parente

Contino

2021

Energy

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The transition towards a sustainable energy sector depends on how we safely manage the transport and storage of energy to keep up with the demand. Large storage (TWh) of renewable energy can be accomplished by producing an energy carrier like ammonia. This power-to-ammonia production process overly depends on the stability of the ammonia reactor where any variations induced by uncertainties could have a large impact on the performance during its dynamic operations. To determine the effect of these variations, we need to identify which of the uncertainties have to be scrutinized during model design. The current work carries out the development of a dynamic Haber-Bosch process, implementing uncertainties in the model and performing an uncertainty quantification analysis on the process. Subsequently, the sensitivity indices quantify the impact of these uncertainties on the design during ramp-up. The global sensitivity analysis indicated that the reactor inlet temperature has the most considerable impact on the performance during ramp-up, where the hydrogen/nitrogen ratio has the second most significant impact. We see that the uncertainty on the

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Multilevel Monte Carlo applied for uncertainty quantification in stochastic multiscale systems

Cited by 17 publications

References 76 publications

Monte Carlo and Kinetic Monte Carlo Models for Deposition Processes: A Review of Recent Works

Monte Carlo and Kinetic Monte Carlo Models for Deposition Processes: A Review of Recent Works

Meta‐heuristic optimization algorithm for predicting software defects

How sensitive is a dynamic ammonia synthesis process? Global sensitivity analysis of a dynamic Haber-Bosch process (for flexible seasonal energy storage)

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