Estimation of spatial uncertainty in material property distributions within heterogeneous structures using optimized convolutional neural networks

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

SciPy 1.0: fundamental algorithms for scientific computing in Python

Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430 · see citing works

Learning Transferable Architectures for Scalable Image Recognition

Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00907 · Cited by 6,631 · see citing works

Stochastic Finite Elements: A Spectral Approach

Ghanem, R., & Spanos, P. D. (1991) · Springer eBooks · doi:10.1007/978-1-4612-3094-6 · Cited by 5,851 · see citing works

The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation

Chicco, D., Warrens, M. J., & Jurman, G. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.623 · Cited by 5,380 · see citing works

Neural Architecture Search with Reinforcement LearningPreprint

Zoph, B., & Le, Q. V. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01578 · Cited by 5,091 · see citing works

On the distribution of points in a cube and the approximate evaluation of integrals

Sobol', I. M. (1967) · USSR Computational Mathematics and Mathematical Physics · doi:10.1016/0041-5553(67)90144-9 · Cited by 2,975 · see citing works

Pymoo: Multi-Objective Optimization in Python

Blank, J., & Deb, K. (2020) · IEEE Access · doi:10.1109/access.2020.2990567 · Cited by 2,407 · see citing works

Neural Architecture Search: A Survey

Elsken, T., Metzen, J. H., & Hutter, F. (2018) · arXiv · doi:10.48550/arxiv.1808.05377 · Cited by 1,763 · see citing works

The stochastic finite element method: Past, present and future

Stefanou, G. (2008) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2008.11.007 · Cited by 1,006 · see citing works

Single Path One-Shot Neural Architecture Search with Uniform Sampling

Guo, Z., Zhang, X., Mu, H., Heng, W., Liu, Z., Wei, Y., & Sun, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58517-4_32 · Cited by 882 · see citing works

Algorithm 659

Bratley, P., & Fox, B. L. (1988) · ACM Transactions on Mathematical Software · doi:10.1145/42288.214372 · Cited by 867 · see citing works

Quantification of uncertainty modelling in stochastic analysis of FRP composites

Sriramula, S., & Chryssanthopoulos, M. K. (2009) · Composites Part A Applied Science and Manufacturing · doi:10.1016/j.compositesa.2009.08.020 · Cited by 317 · see citing works

Proceedings of the ISMA 2016 International Conference on Noise and Vibration Engineering

Titurus, B., Yuan, J. G., Scarpa, F. L., et al. (2016) · Cited by 152 · see citing works

Determination of RVE size for random composites with local volume fraction variation

Savvas, D., Stefanou, G., & Papadrakakis, M. (2016) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2016.03.002 · Cited by 148 · see citing works

Simulation of simply cross correlated random fields by series expansion methods

Vořechovský, M. (2007) · Structural Safety · doi:10.1016/j.strusafe.2007.05.002 · Cited by 143 · see citing works

Parameter identification of a mechanical ductile damage using Artificial Neural Networks in sheet metal forming

Abbassi, F., Belhadj, T., Mistou, S., et al. (2012) · Materials & Design (1980-2015) · doi:10.1016/j.matdes.2012.09.032 · Cited by 137 · see citing works

Structural reliability software at the University of California, Berkeley

Kiureghian, A. D., Haukaas, T., & Fujimura, K. (2005) · Structural Safety · doi:10.1016/j.strusafe.2005.03.002 · Cited by 135 · see citing works

Mechanical property variability in FRP laminates and its effect on failure prediction

Lekou, D. J., & Philippidis, T. P. (2008) · Composites Part B Engineering · doi:10.1016/j.compositesb.2008.01.004 · Cited by 129 · see citing works

A heterogeneous cohesive model for quasi-brittle materials considering spatially varying random fracture properties

Yang, Z., & Xu, F. F. (2008) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2008.03.027 · Cited by 125 · see citing works

Probabilistic strength analysis of rectangular FRP plates using Monte Carlo simulation

Jeong, H. K., & Shenoi, R. A. (2000) · Computers & Structures · doi:10.1016/s0045-7949(99)00171-6 · Cited by 122 · see citing works

Stochastic finite element analysis of composite structures based on mesoscale random fields of material properties

Stefanou, G., Savvas, D., & Papadrakakis, M. (2017) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2017.08.002 · Cited by 121 · see citing works

Direct identification of structural parameters from dynamic responses with neural networks

Xu, B., Wu, Z., Chen, G., et al. (2004) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2004.08.010 · Cited by 109 · see citing works

The need for linking micromechanics of materials with stochastic finite elements: A challenge for materials science

Charmpis, D. C., Schuëller, G. I., & Pellissetti, M. F. (2007) · Computational Materials Science · doi:10.1016/j.commatsci.2007.02.014 · Cited by 107 · see citing works

ANN inverse analysis based on stochastic small-sample training set simulation

Novák, D., & Lehký, D. (2006) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2006.05.003 · Cited by 96 · see citing works

Forward and inverse structural uncertainty propagations under stochastic variables with arbitrary probability distributions

Liu, J., Meng, X., Xu, C., Zhang, D., & Jiang, C. (2018) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2018.07.035 · Cited by 95 · see citing works

Stochastic finite element analysis of composite structures based on material microstructure

Stefanou, G., Savvas, D., & Papadrakakis, M. (2015) · Composite Structures · doi:10.1016/j.compstruct.2015.05.044 · Cited by 84 · see citing works

A phase-field model of fracture with frictionless contact and random fracture properties: Application to thin-film fracture and soil desiccation

Hu, T., Guilleminot, J., & Dolbow, J. E. (2020) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2020.113106 · Cited by 78 · see citing works

Plasticity : Modeling & Computation

주진현 (2014) · 대한토목학회지 · Cited by 75 · see citing works

Sparse identification of time-space coupled distributed dynamic load

Liu, J., & Li, K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.107177 · Cited by 73 · see citing works

Handbook of Probabilistic Models

(2019) · Elsevier eBooks · doi:10.1016/c2017-0-04723-7 · Cited by 70 · see citing works

Simulation of cross-correlated random field samples from sparse measurements using Bayesian compressive sensing

Zhao, T., & Wang, Y. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.04.042 · Cited by 69 · see citing works

Uncertainty propagation in finite deformations––A spectral stochastic Lagrangian approach

Acharjee, S., & Zabaras, N. J. (2005) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2005.05.005 · Cited by 65 · see citing works

Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography

Lähivaara, T., Kärkkäinen, L., Huttunen, J. M. J., & Hesthaven, J. S. (2018) · The Journal of the Acoustical Society of America · doi:10.1121/1.5024341 · Cited by 65 · see citing works

Stochastic Finite Element Methods and Reliability: A State-of-the-Art Report

Sudret, B., Kiureghian, A. D., Sudret, B., et al. (2000) · Cited by 62 · see citing works

Optimal sensor placement for uncertain inverse problem of structural parameter estimation

Liu, J., Ouyang, H., Han, X., et al. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.107914 · Cited by 61 · see citing works

A speckle texture image generator

Orteu, J., Garcia, D., Robert, L., et al. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.695280 · Cited by 56 · see citing works

Uncertainty Quantification in Multiscale Materials Modeling

(2020) · Elsevier eBooks · doi:10.1016/c2018-0-00364-3 · Cited by 54 · see citing works

Representation of random processes using the finite Karhunen-Loève expansion

Fukunaga, K., & Koontz, W. L. G. (1970) · Information and Control · doi:10.1016/s0019-9958(70)80043-2 · Cited by 51 · see citing works

A random field model for anisotropic strain energy functions and its application for uncertainty quantification in vascular mechanics

Staber, B., & Guilleminot, J. (2018) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2018.01.001 · Cited by 51 · see citing works

From SEM images to elastic responses: A stochastic multiscale analysis of UD fiber reinforced composites

Wu, L., Chung, C. N., Major, Z., Adam, L., & Noels, L. (2018) · Composite Structures · doi:10.1016/j.compstruct.2018.01.051 · Cited by 49 · see citing works

Stochastic modeling and identification of a hyperelastic constitutive model for laminated composites

Staber, B., Guilleminot, J., Soize, C., et al. (2019) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2018.12.036 · Cited by 48 · see citing works

Inverse technique identification of material parameters using finite element and neural network computation

Chamekh, A., Belhadjsalah, H., & Hambli, R. (2008) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-008-1809-6 · Cited by 46 · see citing works

Uncertainty quantification of mechanical properties for three-dimensional orthogonal woven composites. Part II: Multiscale simulation

Tao, W., Zhu, P., Xu, C., et al. (2019) · Composite Structures · doi:10.1016/j.compstruct.2019.111764 · Cited by 45 · see citing works

Comparison of different filtering strategies to reduce noise in strain measurement with digital image correlation

Baldoni, J., Lionello, G., Zama, F., et al. (2016) · The Journal of Strain Analysis for Engineering Design · doi:10.1177/0309324716646690 · Cited by 44 · see citing works

Artificial Neural Networks-Based Material Parameter Identification for Numerical Simulations of Additively Manufactured Parts by Material Extrusion

Meißner, P., Watschke, H., Winter, J., & Vietor, T. (2020) · Polymers · doi:10.3390/polym12122949 · Cited by 44 · see citing works

Bayesian identification of Mean-Field Homogenization model parameters and uncertain matrix behavior in non-aligned short fiber composites

Mohamedou, M., Zulueta, K., Chung, C. N., et al. (2019) · Composite Structures · doi:10.1016/j.compstruct.2019.03.066 · Cited by 42 · see citing works

Random field formulation of composite laminates

Wu, W., Cheng, H.-C., & Kang, C.-K. (2000) · Composite Structures · doi:10.1016/s0263-8223(99)00128-2 · Cited by 37 · see citing works

Bayesian identification and model comparison for random property fields derived from material microstructure

Savvas, D., Papaioannou, I., & Stefanou, G. (2020) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2020.113026 · Cited by 36 · see citing works

Uncertainty quantification of mechanical properties for three-dimensional orthogonal woven composites. Part I: Stochastic reinforcement geometry reconstruction

Tao, W., Zhu, P., Xu, C., et al. (2019) · Composite Structures · doi:10.1016/j.compstruct.2019.111763 · Cited by 34 · see citing works

A robust solution of a statistical inverse problem in multiscale computational mechanics using an artificial neural network

Pled, F., Desceliers, C., & Zhang, T. (2020) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2020.113540 · Cited by 33 · see citing works

Data-driven inverse uncertainty quantification in the transverse tensile response of carbon fiber reinforced composites

Balokas, G., Kriegesmann, B., & Rolfes, R. (2021) · Composites Science and Technology · doi:10.1016/j.compscitech.2021.108845 · Cited by 30 · see citing works

An inverse micro-mechanical analysis toward the stochastic homogenization of nonlinear random composites

Wu, L., Nguyen, V. D., Adam, L., et al. (2019) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2019.01.016 · Cited by 27 · see citing works

Stochastic nonlinear analysis of unidirectional fiber composites using image-based microstructural uncertainty quantification

Huang, T., Gao, J., Sun, Q., Zeng, D., Su, X., Liu, W. K., & Chen, W. (2020) · Composite Structures · doi:10.1016/j.compstruct.2020.113470 · Cited by 25 · see citing works

A variable-fidelity hybrid surrogate approach for quantifying uncertainties in the nonlinear response of braided composites

Balokas, G., Kriegesmann, B., Czichon, S., et al. (2021) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2021.113851 · Cited by 25 · see citing works

MULTISCALE IDENTIFICATION OF THE RANDOM ELASTICITY FIELD AT MESOSCALE OF A HETEROGENEOUS MICROSTRUCTURE USING MULTISCALE EXPERIMENTAL OBSERVATIONS

Nguyen, M.-T., Desceliers, C., Soize, C., et al. (2015) · International Journal for Multiscale Computational Engineering · doi:10.1615/intjmultcompeng.2015011435 · Cited by 22 · see citing works

A Bayesian Framework to Identify Random Parameter Fields Based on the Copula Theorem and Gaussian Fields: Application to Polycrystalline Materials

Rappel, H., Wu, L., Noels, L., et al. (2019) · Journal of Applied Mechanics · doi:10.1115/1.4044894 · Cited by 20 · see citing works

Stochastic Modeling and identification of material parameters on structures produced by additive manufacturing

Chu, S., Guilleminot, J., Kelly, C. N., et al. (2021) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2021.114166 · Cited by 20 · see citing works

Probabilistic Finite Element Analysis of ASTM D6641 Compression Test for Marine Grade Polymer Matrix Composites

Nader, J. W., Dagher, H. J., Chiti, F. E., et al. (2008) · Journal of Reinforced Plastics and Composites · doi:10.1177/0731684407087372 · Cited by 13 · see citing works

Interval-based uncertainty models for micromechanical properties of composite materials

Alazwari, M. A., & Rao, S. S. (2018) · Journal of Reinforced Plastics and Composites · doi:10.1177/0731684418788733 · Cited by 12 · see citing works

Probabilistic Finite Element Analysis of Modified ASTM D3039 Tension Test for Marine Grade Polymer Matrix Composites

Nader, J. W., Dagher, H. J., Lopez-Anido, R. A., et al. (2008) · Journal of Reinforced Plastics and Composites · doi:10.1177/0731684407079915 · Cited by 12 · see citing works

Probabilistic mesh-independent discrete damage analyses of laminate composites

Yun, G. J., Zhao, L., & Iarve, E. V. (2015) · Composite Structures · doi:10.1016/j.compstruct.2015.07.083 · Cited by 7 · see citing works

Simulation-based parameter identification of a reduced model using neural networks

Nassef, M. G. A., Schenck, C., & Kuhfuß, B. (2011) · International Conference on Control and Automation · doi:10.1109/icca.2011.6138002 · Cited by 7 · see citing works

Robust Multiscale Identification of Apparent Elastic Properties at Mesoscale for Random Heterogeneous Materials with Multiscale Field Measurements

Zhang, T., Pled, F., & Desceliers, C. (2020) · Materials · doi:10.3390/ma13122826 · Cited by 7 · see citing works

Stochastic Modelling of Additively Manufactured Structures Using a Neural Network for Identification of Random Field Parameters

Pitz, E. J., Rooney, S., & POCHIRAJU, K. V. (2020) · American Society for Composites Technical Conference (ASC) · doi:10.12783/asc35/34974 · Cited by 2 · see citing works

MODELING AND CALIBRATION OF UNCERTAINTY IN MATERIAL PROPERTIES OF ADDITIVELY MANUFACTURED COMPOSITES

Pitz, E. J., Rooney, S., & POCHIRAJU, K. V. (2021) · American Society for Composites Technical Conference (ASC) · doi:10.12783/asc36/35758 · Cited by 2 · see citing works

A comprehensive review of lunar-based manufacturing and construction

Azami, M., Kazemi, Z., Moazen, S., Dubé, M., Potvin, M.-J., & Skonieczny, K. (2024) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2024.101045 · Cited by 57 · see citing works

Calibration of a Digital Image Correlation System

Sebastian, C. M., & Patterson, E. A. (2012) · Experimental Techniques · doi:10.1111/ext.12005 · Cited by 36 · see citing works

Uncertainty propagation in structural reliability with implicit limit state functions under aleatory and epistemic uncertainties

Zhou, S., Zhang, J., You, L., et al. (2021) · Eksploatacja i Niezawodnosc - Maintenance and Reliability · doi:10.17531/ein.2021.2.3 · Cited by 6 · see citing works

Reliability on calibration of CMM

Makoto, A. B. B. E., Takamasu, K., & Ozono, S. (2003) · Measurement · doi:10.1016/s0263-2241(03)00012-5 · Cited by 21 · see citing works

Arguments for the Generality and Effectiveness of “Discrete Direct” Model Calibration and Uncertainty Propagation vs. Other Calibration-UQ Approaches

Romero, V. (2022) · AIAA SciTech Forum · doi:10.2514/6.2022-2107

A method for uncertainty quantification in the life prediction of gas turbine components

(1999) · ESIS publication · doi:10.1016/s1566-1369(99)80027-2 · Cited by 3 · see citing works