A Runtime Library for Platform-Independent Task Parallelism

Hadjidoukas, Panagiotis E.; Lappas, E.; Dimakopoulos, Vassilios V.

doi:10.1109/pdp.2012.89

Cited by 21 publications

(12 citation statements)

References 15 publications

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“…[105], the authors applied this approach in the path-space measure of molecular dynamics simulations, in order to obtain optimized coarse-grained molecular models for both equilibrium and non-equilibrium simulations. where Bayesian tools were expressed as engines upon the layer of the TORC [109] tasking library. TORC works by defining a set of workers, each spanning multiple processor cores, distributed throughout a computing cluster and assigning them a fixed set of model evaluations as guided by the stochastic method.…”

Section: Approximation Of Intractable Posterior Pdfsmentioning

confidence: 99%

Bayesian calibration of force fields for molecular simulations

Cailliez¹,

Pernot²,

Rizzi³

et al. 2020

Uncertainty Quantification in Multiscale Materials Modeling

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Molecular simulations are one of the most prominent discovery tools in science and engineering, widely adopted in applications ranging from drug discovery to materials design. The fidelity of the predictions of molecular simulations hinges on the reliability and accuracy of the chosen force fields. Thus, the quantification of uncertainties associated with the form of force fields and their parameters is a fundamental part of molecular modeling. This aspect has been rather overlooked in the past but constitutes today an active field of research. This review focuses on the data driven, Bayesian calibration of force fields. The calibration of molecular force fields is a very challenging task in the presence of an ever increasing amount, type and scale of reference data. Bayesian data analysis provides an invaluable tool to address this challenge, notably in the presence of model inadequacy. It enables a rigorous definition of uncertainties for the force fields and allows for data driven, robust predictions through molecular simulations. This chapter reviews recent methodological and algorithmic developments in Bayesian calibration of force fields, and their application to atomic and molecular fluids. We discuss Bayesian approaches and their computational challenges followed by a description of target applications. We believe that the advances in Bayesian inference presented in this article would be valuable for a number of other fields in Science and Engineering, that are revolutionized by Data science.

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Section: Approximation Of Intractable Posterior Pdfsmentioning

confidence: 99%

Bayesian calibration of force fields for molecular simulations

Cailliez¹,

Pernot²,

Rizzi³

et al. 2020

Uncertainty Quantification in Multiscale Materials Modeling

View full text Add to dashboard Cite

show abstract

“…Such a computation is performed using the non-intrusive adaptive Parallel Numerical Differentiation Library (PNDL). 24 The above optimization algorithm is implemented within Π4U framework 18 based on a state-of-the-art task-parallel library for clusters, called TORC, 25 which is designed to provide unified programming and runtime support for computing platforms that range from single-core systems to hybrid multicore-Graphics Processing Unit (GPU) clusters and heterogeneous grid-based supercomputers.…”

Section: Introductionmentioning

confidence: 99%

A model-based fatigue damage estimation framework of large-scale structural systems

Giagopoulos

Αραϊλόπουλος

Natsiavas

2019

Structural Health Monitoring

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A model-based fatigue damage estimation framework is proposed for online estimation of fatigue damage, for structural systems by integrating operational vibration measurements in a high-fidelity, large-scale, finite element (FE) model and applying a fatigue damage accumulation methodology. To proceed with fatigue predictions, one has to infer the stress response time histories characteristics based on the monitoring information contained in vibration measurements collected from a limited number of sensors attached to a structure. Predictions, like the existence, the location, the time, and the extent of the damage, are possible if one combines the information in the measurements with information obtained from a high-fidelity FE model of the structure. Such a model may be optimized with respect to the data, using state-of-the-art FE model updating techniques. These methods provide much more comprehensive information about the condition of the monitored system than the analysis of raw data. The diagnosed degradation state, along with its identified uncertainties, can be incorporated into robust reliability tools for updating predictions of the residual useful lifetime of structural components and safety against various failure modes taking into account stochastic models of future loading characteristics. Fatigue is estimated using the Palmgren–Miner damage rule, S-N curves, and rainflow cycle counting of the variable amplitude time histories of the stress components. Incorporating a numerical model of the structure in the response estimation procedure, permits stress estimation at unmeasured spots. The proposed method is applied in a steel frame of a real city bus.

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“…Such a computation is performed, using the non-intrusive adaptive Parallel Numerical Differentiation Library (PNDL). 44 The above optimization algorithm is implemented within Π4U framework 38 based on a state-of-the-art task-parallel library for clusters called TORC, 45 which is designed to provide unified programming and runtime support for computing platforms that range from single-core systems to hybrid multicore-Graphics Processing Unit (GPU) clusters and heterogeneous grid-based supercomputers.…”

Section: Introductionmentioning

confidence: 99%

Structural health monitoring and fatigue damage estimation using vibration measurements and finite element model updating

Giagopoulos

Αραϊλόπουλος

Dertimanis

et al. 2018

Structural Health Monitoring

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In this work, a computational framework is proposed for fatigue damage estimation in structural systems by integrating operational experimental measurements in a high-fidelity, large-scale finite element model. The proposed method is applied in a linear steel substructure of a lignite grinder assembly at a Public Power Corporation power plant. A finite element model of the steel base is developed and updated to match the dynamic characteristics measured in real operating conditions. This is achieved through coupled use of numerical and experimental methods for identifying, updating, and optimizing a high-fidelity finite element model. The full stress time histories of the complex mechanical assembly are estimated, at critical locations, by imposing operational vibration measurements from a limited number of sensors in the updated finite element model. Fatigue damage and remaining lifetime is subsequently estimated via commonly adopted engineering approaches, such as Palmgren-Miner damage rule, S-N curves, and rainflow cycle counting. Incorporation of a numerical model of the structure in the response estimation procedure permits stress estimation at unmeasured locations, thereby enabling the drawing of a complete and substantially dense fatigue map consistent with the vibration measurements. Fatigue predictions via the proposed framework are highly correlated to experimental fatigue results, proving the efficiency and applicability of the framework.

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A Runtime Library for Platform-Independent Task Parallelism

Cited by 21 publications

References 15 publications

Bayesian calibration of force fields for molecular simulations

Bayesian calibration of force fields for molecular simulations

A model-based fatigue damage estimation framework of large-scale structural systems

Structural health monitoring and fatigue damage estimation using vibration measurements and finite element model updating

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