Integrated stochastic analysis of fiber composites manufacturing using adapted polynomial chaos expansions

Ghauch, Ziad G.; Aitharaju, Venkat; Rodgers, William R.; Pasupuleti, Praveen; Dereims, Arnaud; Ghanem, Roger

doi:10.1016/j.compositesa.2018.12.029

Cited by 25 publications

(3 citation statements)

References 28 publications

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“…The methodology of Polynomial Chaos Expansion (PCE) is found to be extensively used to quantify the unavoidable uncertainties that exist in the matrix and the fiber which consequently affect the material properties and their global responses [115][116][117]. This expansion is also known as Wiener Chaos expansion and is an effective technique for solving stochastic systems.…”

Section: Prediction Optimization and Uncertainty Quantificationmentioning

confidence: 99%

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Sharma

Mukhopadhyay

Rangappa

et al. 2022

Arch Computat Methods Eng

108

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The superior multi-functional properties of polymer composites have made them an ideal choice for aerospace, automobile, marine, civil, and many other technologically demanding industries. The increasing demand of these composites calls for an extensive investigation of their physical, chemical and mechanical behavior under different exposure conditions. Machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modeling, leading to unprecedented insights and exploration of the system properties beyond the capability of traditional computational and experimental analyses. Here we aim to abridge the findings of the large volume of relevant literature and highlight the broad spectrum potential of ML in applications like prediction, optimization, feature identification, uncertainty quantification, reliability and sensitivity analysis along with the framework of different ML algorithms concerning polymer composites. Challenges like the curse of dimensionality, overfitting, noise and mixed variable problems are discussed, including the latest advancements in ML that have the potential to be integrated in the field of polymer composites.Based on the extensive literature survey, a few recommendations on the exploitation of various ML algorithms for addressing different critical problems concerning polymer composites are provided along with insightful perspectives on the potential directions of future research.

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Section: Prediction Optimization and Uncertainty Quantificationmentioning

confidence: 99%

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Sharma

Mukhopadhyay

Rangappa

et al. 2022

Arch Computat Methods Eng

108

View full text Add to dashboard Cite

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“…It is evident at this point that as the number of random inputs increase, the size of the vector of coefficients c increases beyond what practical numerical recipes can efficiently solve. To address this issue, basis adaption techniques have been developed [12], and applied successfully in forward [6], inverse [13,5], and optimization under uncertainty problems [11]. Tipireddy and Ghanem [12] review in detail the theoretical background behind basis-adapted pc.…”

Section: Basis-adapted Polynomial Chaosmentioning

confidence: 99%

Efficient polynomial chaos approximations: Active, local and basis-adapted

Ghauch

2021

ANZIAMJ

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Metamodels provide an efficient means for the approximation of response surfaces of systems, particularly for resource-intensive experiment designs. It is oftentimes the case that interest is focused on a specific region of the parameter space. We propose an efficient recipe for the local approximation of response surfaces using Polynomial Chaos techniques. For systems embedded in high-dimensional settings, a basis-adapted spectral representation is exploited locally for dimension reduction. The proposed approach comprises an initial heuristic global solution for parameter space exploration using an approximate global Polynomial Chaos metamodel, followed by a local design being refined through an active learning scheme. The problem of turbulent flow around a symmetric airfoil is considered. Statistical estimators based on the local, active, basis-adapted approach show less bias and faster convergence as compared to the estimators from a global solution. References B. J. Bichon, M. S. Eldred, L. P. Swiler, S. Mahadevan, and J. M. McFarland. Efficient global reliability analysis for nonlinear implicit performance functions. AIAA J. 46(10):2459–2468, 2008. doi: 10.2514/1.34321. G. E. P. Box and N. R. Draper. Empirical Model-Building and Response Surfaces. Wiley, 1987. V. Dubourg, B. Sudret, and F. Deheeger. Metamodel-based importance sampling for structural reliability analysis. Prob. Eng. Mech. 33:47–57, 2013. doi: 10.1016/j.probengmech.2013.02.002. R. G. Ghanem and P. D. Spanos. Stochastic finite element: A spectral approach. Dover, 1991. doi: 10.1007/978-1-4612-3094-6. Z. G. Ghauch. Leveraging adapted polynomial chaos metamodels for real-time Bayesian updating. J. Verif. Valid. Uncert. 4(4):041003, 2020. doi: 10.1115/1.4045693. Z. G. Ghauch, V. Aitharaju, W. R. Rodgers, P. Pasupuleti, A. Dereims, and R. G. Ghanem. Integrated stochastic analysis of fiber composites manufacturing using adapted polynomial chaos expansions. Compos. Part A: Appl. Sci. 118:179–193, 2019. doi: 10.1016/j.compositesa.2018.12.029. M. E. Johnson, L. M. Moore, and D. Ylvisaker. Minimax and maximin distance designs. J. Stat. Plan. Infer. 26(2):131–148, 1990. doi: 10.1016/0378-3758(90)90122-B. A. Notin, N. Gayton, J. L. Dulong, M. Lemaire, P. Villon, and H. Jaffal. RPCM: A strategy to perform reliability analysis using polynomial chaos and resampling. Euro. J. Comput. Mech. 19(8):795–830, 2010. doi: 10.3166/ejcm.19.795-830. OpenCFD. OpenFOAM User’s Guide. 2019. https://www.openfoam.com/documentation/user-guide. V. Picheny, D. Ginsbourger, O. Roustant, R. T Haftka, and N.-H. Kim. Adaptive designs of experiments for accurate approximation of target regions. J. Mech. Design. 132(7):071008, 2010. doi: 10.1115/1.4001873. C. Thimmisetty, P. Tsilifis, and R. Ghanem. Homogeneous chaos basis adaptation for design optimization under uncertainty: Application to the oil well placement problem. AI EDAM 31(3):265–276, 2017. doi: 10.1017/S0890060417000166. R. Tipireddy and R. Ghanem. Basis adaptation in homogeneous chaos spaces. J. Comput. Phys. 259:304–317, 2014. doi: 10.1016/j.jcp.2013.12.009. P. Tsilifis and R. G. Ghanem. Reduced Wiener chaos representation of random fields via basis adaptation and projection. J. Comput. Phys. 341:102–120, 2017. doi: 10.1016/j.jcp.2017.04.009. Turbulence Modeling Resource. NASA Langley Research Center. Washington, DC, 2018. http://turbmodels.larc.nasa.gov/.

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“…Random number sequences have immense impact on numerous applications, such as signal processing [1,2], stochastic simulations [3,4], spread spectrums [5,6], gaming [7][8][9], statistics [10], captcha [11,12], machine learning [13,14], and cryptography etc. The random number generators that pertain to excellent statistical properties are considered critical for robust cryptographic applications [15].…”

Section: Introductionmentioning

confidence: 99%

Pseudorandom Number Generator (PRNG) Design Using Hyper-Chaotic Modified Robust Logistic Map (HC-MRLM)

et al. 2020

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Robust chaotic systems, due to their inherent properties of mixing, ergodicity, and larger chaotic parameter space, constitute a perfect candidate for cryptography. This paper reports a novel method to generate random numbers using modified robust logistic map (MRLM). The non-smooth probability distribution function of robust logistic map (RLM) trajectories gives an un-even binary distribution in randomness test. To overcome this disadvantage in RLM, control of chaos (CoC) is proposed for smooth probability distribution function of RLM. For testing the proposed design, cryptographic random numbers generated by MRLM were vetted with National Institute of Standards and Technology statistical test suite (NIST 800-22). The results showed that proposed MRLM generates cryptographically secure random numbers (CSPRNG).

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Integrated stochastic analysis of fiber composites manufacturing using adapted polynomial chaos expansions

Cited by 25 publications

References 28 publications

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Efficient polynomial chaos approximations: Active, local and basis-adapted

Pseudorandom Number Generator (PRNG) Design Using Hyper-Chaotic Modified Robust Logistic Map (HC-MRLM)

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