Publications

Book

V. Papadopoulos, D.G. Giovanis, Stochastic Finite Element Methods: An Introduction, Mathematical Engineering, Springer, ISBN:978-3-319-64527-8, 2018.

Preprints

T. J. Hardin, M. Chandross, R. Meena, S. Fajardo, D.G. Giovanis, Y. G. Kevrekidis, M. Falk, M. Shields, , Quantifying the Structure of Disordered Materials, Submitted to Nature Communications, (December 2022).

Journal Articles

K. Upadhya, D.G. Giovanis, R. Jagani, A. Alshareef, A.K. Knutsen, C. L. Johnson, A. Caras, P.V. Bayly, M.D. Shields, K.T. Ramesh, Data-driven Uncertainty Quantification in Computational Human Head Models, Computer Methods in Applied Mechanics and Engineering, Vol.398, 115108, 2022.

K.R.M. dos Santos, D.G. Giovanis, K. Kontolati, D. Loukrezis, M.D. Shields, Grassmannian diffusion maps based surrogate modeling via geometric harmonics, International Journal for Numerical Methods in Engineering, 1-23, 2022.

D.G. Giovanis, M.D. Shields, Imprecise Subset Simulation, Probabilistic Engineering Mechanics, Vol.69, 103293, 2022.

K. Kontolati, D. Loukrezis, D.G. Giovanis, L. Vandanapu, M.D. Shields, A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems, Journal of Computational Physics, Vol.464, 111313, 2022.

K. Kontolati, D. Loukrezis, K.R.M. dos Santos, D.G. Giovanis, M D. Shields, Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models, International Journal for Uncertainty Quantification, Vol.12(4), 39-64, 2022.

K.R.dos Santos, D.G. Giovanis, M.D. Shields, Grassmannian Diffusion Maps–Based Dimension Reduction and Classification for High-Dimensional Data, SIAM Journal on Scientific Computing, Vol.44(2), B250-B274, 2022.

M.D. Shields, D.G. Giovanis, V.S. Sundar, Subset Simulation for problems with strongly non-Gaussian, highly anisotropic, and degenerate distributions, Computers & Structures, Vol.45(3), 106431, 2021.

D.G. Giovanis, M.D. Shields, Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold, Computer Methods in Applied Mechanics and Engineering, Vol.370, 113269, 2020.

X. Lu, D.G. Giovanis, J. Yvonnet, V. Papadopoulos, F. Detrez, J. Bai, A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites, Computational Mechanics, Vol.64(2), 307-321, 2019.

V. Papadopoulos, I. Kalogeris, D.G. Giovanis, A spectral stochastic formulation for nonlinear framed structures, Probabilistic Engineering Mechanics, Vol.55, 90-101, 2019.

D.G. Giovanis, M.D. Shields, Variance-based simplex stochastic collocation with model order reduction for high-dimensional systems, International Journal for Numerical Methods in Engineering, Vol.117(11), 1-38, 2018.

D.G. Giovanis, M.D. Shields, Uncertainty quantification for complex systems with very high dimensional response using Grassmann manifold variations, Journal of Computational Physics, Vol.364, 393-415, 2018.

V. Papadopoulos, G. Soimoiris, D.G. Giovanis, A neural network-based surrogate model for carbon nanotubes with geometrical nonlinearities, Computer Methods in Applied Mechanics and Engineering, Vol.328, 411-430, 2017.

G. Stavroulakis, D.G. Giovanis, V. Papadopoulos, M. Papadrakakis, A GPU domain decomposition solution for spectral stochastic finite element method, Computer Methods in Applied Mechanics and Engineering, Vol.327, 392-410, 2017.

D.G. Giovanis, I. Papaioannou, D. Straub, V. Papadopoulos, Bayesian updating with subset simulation using artificial neural networks, Computer Methods in Applied Mechanics and Engineering, Vol.319, 124-145, 2017.

D.G. Giovanis, M. Fragiadakis, V. Papadopoulos, Assessment of epistemic uncertainty using incremental dynamic analysis and artificial neural networks, Bulletin of Earthquake Engineering, Vol.14(2), 529-547, 2016.

D.G. Giovanis, V. Papadopoulos, G. Stavroulakis, An adaptive spectral Galerkin stochastic finite element method using variability response functions, International Journal for Numerical Methods in Engineering, Vol.104(3), 185-208, 2015.

D.G. Giovanis, V. Papadopoulos, Spectral representation-based neural network assisted stochastic structural mechanics, Engineering Structures, Vol.104, 382-394, 2015.

G. Stavroulakis, D.G. Giovanis, M. Papadrakakis, V. Papadopoulos, A new perspective on the solution of uncertainty quantification and reliability analysis of large-scale problems, Computer Methods in Applied Mechanics and Engineering, Vol.276, 627-658, 2014.

V. Papadopoulos, D.G. Giovanis, N.D. Lagaros, M. Papadrakakis, Accelerated subset simulation with neural networks for reliability analysis, Computer Methods in Applied Mechanics and Engineering, Vol.223, 70-80, 2012.

In Conference Proceedings

D.G. Giovanis, M.D. Shields, Imprecise Subset simulation for reliability analysis, 9th International Workshop on Reliable Engineering Computing (REC2021), Taormina, Italy, 2021.

D.G. Giovanis, M.D. Shields, Structural reliability analysis from sparse data, 3th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP13), Seoul, S. Korea, 2019.

D.G. Giovanis, M.D. Shields, High-dimensional interpolation on the Grassmann manifold using Gaussian processes, 3th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP13), Seoul, S. Korea, 2019.

D.G. Giovanis, M.D. Shields, Regression of High Dimensional Data on the Grassmann Manifold using Spectral Clustering, Eighth International Conference on Computational Stochastic Mechanics (CSM8), Paros, Greece, 2018.

D.G. Giovanis, V. Papadopoulos, N.D. Lagaros, M. Papadrakakis Structural reliability analysis using subset simulation and neural networks, The 10th International Conference on Structural Safety and Reliability (ICOSSAR), Paros, Greece, 2018.