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895 results on '"Shields, Michael D."'

59. Coarse graining atomistic simulations of plastically deforming amorphous solids.

Author

Hinkle, Adam R, Rycroft, Chris H, Shields, Michael D, and Falk, Michael L

Abstract

The primary mode of failure in disordered solids results from the formation and persistence of highly localized regions of large plastic strains known as shear bands. Continuum-level field theories capable of predicting this mechanical response rely upon an accurate representation of the initial and evolving states of the amorphous structure. We perform molecular dynamics simulations of a metallic glass and propose a methodology for coarse graining discrete, atomistic quantities, such as the potential energies of the elemental constituents. A strain criterion is established and used to distinguish the coarse-grained degrees-of-freedom inside the emerging shear band from those of the surrounding material. A signal-to-noise ratio provides a means of evaluating the strength of the signal of the shear band as a function of the coarse graining. Finally, we investigate the effect of different coarse graining length scales by comparing a two-dimensional, numerical implementation of the effective-temperature description in the shear transformation zone (STZ) theory with direct molecular dynamics simulations. These comparisons indicate the coarse graining length scale has a lower bound, above which there is a high level of agreement between the atomistics and the STZ theory, and below which the concept of effective temperature breaks down.

Published
2017

60. Validating clinical practice guidelines for the management of children with non-blanching rashes in the UK (PiC): a prospective, multicentre cohort study

Author

Kitching, Amy, Rotheram, Matthew, Robinson, Gisela, Brassey, Paula, Hartshorn, Stuart, Wane, Rachel, Lyttle, Mark, Dangerfield, Jo, Hayes, Michael, McFarlane, Rebecca, Armstrong, Helen, Roland, Damian, Smith, Sally, VanHeyningen, Carl, Wilson, Esther, Kehler, Lisa, Gough, Christopher, Scott, Fraser, Backhouse, Claire, Gomes, Sylvester, Wood, Darryl, Maney, Julie-Ann, Johnson, Graham, Foster, Steven, Bloom, Ben, Lancaster, Andrew, Gray, Sebastian, Ramlakhan, Shammi, Gardner, Sharryn, Floyd, Sharon, Cleaver, Chris, MacFarlane, Susan, Bell, Claire, Nyirenda, Maggie, Bayreuther, Jane, Ijaz, Asim, Rogers, Natalie, Wilson, Sarah, Diment, Sarah, Boulind, Caroline, Allison, Kathryn, Waterfield, Thomas, Fairley, Derek, McKenna, James, Corr, Michael, McFetridge, Lisa, Mitchell, Hannah, Woolfall, Kerry, Lynn, Fiona, Petenall, Bethany, Shields, Michael, Maney, Juli-Ann, Lyttle, Mark D, McKenna, James P, Patenall, Bethany, and Shields, Michael D

Published
2021
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63. The generalization of Latin hypercube sampling

Author

Shields, Michael D. and Zhang, Jiaxin

Subjects
Statistics - Computation
Abstract

Latin hypercube sampling (LHS) is generalized in terms of a spectrum of stratified sampling (SS) designs referred to as partially stratified sample (PSS) designs. True SS and LHS are shown to represent the extremes of the PSS spectrum. The variance of PSS estimates is derived along with some asymptotic properties. PSS designs are shown to reduce variance associated with variable interactions, whereas LHS reduces variance associated with main effects. Challenges associated with the use of PSS designs and their limitations are discussed. To overcome these challenges, the PSS method is coupled with a new method called Latinized stratified sampling (LSS) that produces sample sets that are simultaneously SS and LHS. The LSS method is equivalent to an Orthogonal Array based LHS under certain conditions but is easier to obtain. Utilizing an LSS on the subspaces of a PSS provides a sampling strategy that reduces variance associated with both main effects and variable interactions and can be designed specially to minimize variance for a given problem. Several high-dimensional numerical examples highlight the strengths and limitations of the method. The Latinized partially stratified sampling method is then applied to identify the best sample strategy for uncertainty quantification on a plate buckling problem., Comment: 31 pages, 9 figures

Published
2015
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64. Refined Stratified Sampling for efficient Monte Carlo based uncertainty quantification

Author

Shields, Michael D., Teferra, Kirubel, Hapij, Adam, and Daddazio, Raymond P.

Subjects
Statistics - Methodology
Abstract

A general adaptive approach rooted in stratified sampling (SS) is proposed for sample-based uncertainty quantification (UQ). To motivate its use in this context the space-filling, orthogonality, and projective properties of SS are compared with simple random sampling and Latin hypercube sampling (LHS). SS is demonstrated to provide attractive properties for certain classes of problems. The proposed approach, Refined Stratified Sampling (RSS), capitalizes on these properties through an adaptive process that adds samples sequentially by dividing the existing subspaces of a stratified design. RSS is proven to reduce variance compared to traditional stratified sample extension methods while providing comparable or enhanced variance reduction when compared to sample size extension methods for LHS - which do not afford the same degree of flexibility to facilitate a truly adaptive UQ process. An initial investigation of optimal stratification is presented and motivates the potential for major advances in variance reduction through optimally designed RSS. Potential paths for extension of the method to high dimension are discussed. Two examples are provided. The first involves UQ for a low dimensional function where convergence is evaluated analytically. The second presents a study to asses the response variability of a floating structure to an underwater shock.

Published
2015
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82. UQpy v4.1: Uncertainty quantification with Python

Author

Tsapetis, Dimitrios, primary, Shields, Michael D., additional, Giovanis, Dimitris G., additional, Olivier, Audrey, additional, Novak, Lukas, additional, Chakroborty, Promit, additional, Sharma, Himanshu, additional, Chauhan, Mohit, additional, Kontolati, Katiana, additional, Vandanapu, Lohit, additional, Loukrezis, Dimitrios, additional, and Gardner, Michael, additional

Published
2023
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92. Medication Adherence in Children with Asthma.

Author

McCrossan, Paddy, Shields, Michael D, and McElnay, James C

Subjects
*ASTHMA in children, *PATIENT compliance, *ASTHMATICS, *JUVENILE diseases, *ASTHMA, *WHEEZE, *VOCAL cord dysfunction
Abstract

Asthma is the most common chronic disease in childhood. If untreated, asthma can lead to debilitating daily symptoms which affect quality of life, but more importantly can lead to fatal asthma attacks which unfortunately still occur globally. The most effective treatment strategy for controlling asthma is for the patient to follow a personalised asthma action plan (PAAP) which will invariably include regular use of an inhaled corticosteroid. To examine medication adherence in children with asthma, we collated recent evidence from systematic reviews in this area to address the following 5 key questions; What is adherence? Is there evidence that children are not adhering to preventer medication? Why is adherence poor and what are the barriers to adherence? Does good adherence improve outcomes in asthma? And lastly, how can treatment adherence be improved? [ABSTRACT FROM AUTHOR]

Published
2024
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100. Model order reduction in subset simulations using the proper orthogonal decomposition.

Author

Thaler, Denny, Shields, Michael D., Markert, Bernd, and Bamer, Franz

Subjects
*PROPER orthogonal decomposition, *MONTE Carlo method, *FAILURE mode & effects analysis
Abstract

The crude Monte Carlo method is computationally expensive. Hence, incorporating model order reduction methods enabling reliability analysis for high‐dimensional problems is necessary. However, this strategy may result in an inaccurate estimation of the probability of failure for rare events for two reasons. First, the model order reduction, represented by the proper orthogonal decomposition (POD) here, requires response information in the form of snapshots a priori. To capture the essential nonlinear response behavior, we propose to update the proper orthogonal modes using extreme events. Second, the crude Monte Carlo simulation requires many samples to estimate low failure probabilities reliably. To this end, subset simulation found wide application in reliability analysis to reduce computational effort. Following this strategy, the proposed samples gradually move toward the failure region. Thus, incorporating updates of the modes is particularly promising in evaluating samples from the current subset region. This contribution shows the computational efficiency of POD within subset simulations. We then propose to leverage the estimation of the probability of failure by updating the modes within each subset. [ABSTRACT FROM AUTHOR]

Published
2023
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