Uncertainty Quantification in Atomistic Modeling of Metals and Its Effect on Mesoscale and Continuum Modeling: A Review

Gabriel, Joshua J.; Paulson, Noah H.; Duong, Thien; Tavazza, Francesca; Becker, Chandler A.; Chaudhuri, Santanu; Stan, Marius

doi:10.1007/s11837-020-04436-6

Cited by 12 publications

(4 citation statements)

References 100 publications

(130 reference statements)

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“…This topic is the focus of the Integrated Computational Materials Engineering (ICME) domain, which has received enormous attention in recent years [12]. Research in ICME is inherently multi-scale, starting with molecular dynamics or CALPHAD (CALculation of PHAse Diagrams) simulations for nano-scale properties [13,14]. Results become inputs for higher level models that compute micro-scale properties, ending with continuum models that can be used for macro-scale simulations.…”

Section: Design With As-manufactured Propertiesmentioning

confidence: 99%

The Current Design for Additive Manufacturing Research Frontier

Rosen

2024

Int. J. Precis. Eng. Manuf.-Smart Tech.

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Design for additive manufacturing (DFAM) seeks to develop designs that take advantage of the unique capabilities of additive manufacturing (AM) processes to maximize benefits. In this paper, several issues at the frontier of DFAM research are highlighted. First, the need is described to include as-manufactured mechanical and other physical properties, such as topology and shape optimization and generative design, during computational design. AM processes rarely produce parts with homogeneous composition and isotropic properties, so design methods and tools should not assume them. Second, the topic of designing for the AM process chain is important since AM-fabricated parts typically need postprocessing operations, such as support removal, finish machining, heat treatments, etc. DFAM methods need to incorporate the entire process chain, not just the AM process, which is explored with an example from metal powder bed fusion. Third, potentially, the largest benefit of AM can be realized by radically rethinking product architectures. Examples of an electric motorcycle and an assistive exosuit illustrate the ideas and potential benefits. Finally, some thoughts on design for 4D printing are offered, specifically for 3D printed morphing and deployable systems that utilize the shape memory effect.

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Section: Design With As-manufactured Propertiesmentioning

confidence: 99%

The Current Design for Additive Manufacturing Research Frontier

Rosen

2024

Int. J. Precis. Eng. Manuf.-Smart Tech.

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“…. , d where j ą 0, with e j as defined in equation (9). In other words, in an admissible index set, all indices with smaller entries in at least one direction are also included in the set.…”

Section: Dimension-adaptive Multi-index Monte Carlomentioning

confidence: 99%

“…Given the critical importance of UQ for a wide variety of problems in materials science, several frameworks have been developed to provide robust predictions under uncertainty, see e.g., [5,6,7]. Comprehensive reviews of UQ applications in ICME-based simulations can be found in Honarmandi and Arróyave [8], Gabriel et al [9], and Acar [10]. For example, Zhao et al [11] incorporated measurement and parametric uncertainty to quantify the uncertainty of critical resolved shear stress for hexagonal close-packed (HCP) Ti alloys from nano-indentation.…”

Section: Introductionmentioning

confidence: 99%

Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods

Tran¹,

Robbe²,

Lim³

2023

Preprint

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Quantifying uncertainty associated with the microstructure variation of a material can be a computationally daunting task, especially when dealing with advanced constitutive models and fine mesh resolutions in the crystal plasticity finite element method (CPFEM). Numerous studies have been conducted regarding the sensitivity of material properties and performance to the mesh resolution and choice of constitutive model. However, a unified approach that accounts for various fidelity parameters, such as mesh resolutions, integration time-steps, and constitutive models simultaneously is currently lacking. This paper proposes a novel uncertainty quantification (UQ) approach for computing the properties and performance of homogenized materials using CPFEM, that exploits a hierarchy of approximations with different levels of fidelity. In particular, we illustrate how multi-level sampling methods, such as multi-level Monte Carlo (MLMC) and multi-index Monte Carlo (MIMC), can be applied to assess the impact of variations in the microstructure of polycrystalline materials on the predictions of homogenized materials properties. We show that by adaptively exploiting the fidelity hierarchy, we can significantly reduce the number of microstructures required to reach a certain prescribed accuracy. Finally, we show how our approach can be extended to a multi-fidelity framework, where we allow the underlying constitutive model to be chosen from either a phenomenological plasticity model or a dislocation-density-based model.

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“…It is an obstacle for CC to stand on par with, or to replace, physical measurements 8 or to be used in decision making 9 . It has also a strong impact in multi-scale simulation, where propagation of uncertainty through the scales is necessary to assess the reliability of predictions [10][11][12] , or in iterative learning to minimize the cost of high-level calculations [13][14][15] . In benchmarking, a very sensible way to select among a set of levels of theory would be to pick one with a fit-to-purpose PU.…”

Section: Introductionmentioning

confidence: 99%

The long road to calibrated prediction uncertainty in computational chemistry

Pernot

2022

Preprint

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a) Uncertainty quantification (UQ) in computational chemistry (CC) is still in its infancy.Very few CC methods are designed to provide a confidence level on their predictions, and most users still rely improperly on the mean absolute error as an accuracy metric. The development of reliable uncertainty quantification methods is essential, notably for computational chemistry to be used confidently in industrial processes. A review of the CC-UQ literature shows that there is no common standard procedure to report nor validate prediction uncertainty. I consider here analysis tools using concepts (calibration and sharpness) developed in meteorology and machine learning for the validation of probabilistic forecasters. These tools are adapted to CC-UQ and applied to datasets of prediction uncertainties provided by composite methods, Bayesian Ensembles methods, machine learning and a posteriori statistical methods.

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Uncertainty Quantification in Atomistic Modeling of Metals and Its Effect on Mesoscale and Continuum Modeling: A Review

Cited by 12 publications

References 100 publications

The Current Design for Additive Manufacturing Research Frontier

The Current Design for Additive Manufacturing Research Frontier

Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods

The long road to calibrated prediction uncertainty in computational chemistry

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