| Discovering and forecasting extreme events via active learning in neural operators E Pickering, S Guth, GE Karniadakis, TP Sapsis Nature Computational Science 2 (12), 823-833, 2022 | 64 | 2022 |
| Machine learning predictors of extreme events occurring in complex dynamical systems S Guth, TP Sapsis Entropy 21 (10), 925, 2019 | 45 | 2019 |
| Experimental study of electromagnetic Bessel-Gaussian Schell Model beams propagating in a turbulent channel S Avramov-Zamurovic, C Nelson, S Guth, O Korotkova, R Malek-Madani Optics Communications 359, 207-215, 2016 | 38 | 2016 |
| Flatness parameter influence on scintillation reduction for multi-Gaussian Schell-model beams propagating in turbulent air S Avramov-Zamurovic, C Nelson, S Guth, O Korotkova Applied optics 55 (13), 3442-3446, 2016 | 29 | 2016 |
| Scintillation reduction in pseudo Multi-Gaussian Schell Model beams in the maritime environment C Nelson, S Avramov-Zamurovic, O Korotkova, S Guth, R Malek-Madani Optics Communications 364, 145-149, 2016 | 27 | 2016 |
| Wave episode based Gaussian process regression for extreme event statistics in ship dynamics: Between the Scylla of Karhunen–Loève convergence and the Charybdis of transient … S Guth, TP Sapsis Ocean Engineering 266, 112633, 2022 | 14 | 2022 |
| Lagrangian and Eulerian analysis of transport and mixing in the three dimensional, time dependent Hill’s spherical vortex KL McIlhany, S Guth, S Wiggins Physics of Fluids 27 (6), 2015 | 13 | 2015 |
| Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine monopile foundations using wave episodes and targeted CFD simulations through active sampling S Guth, E Katsidoniotaki, TP Sapsis Wind Energy 27 (1), 75-100, 2024 | 12* | 2024 |
| Quality measures for the evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems S Guth, A Mojahed, TP Sapsis Computer Methods in Applied Mechanics and Engineering 420, 116760, 2024 | 5* | 2024 |
| Surrogate model of a wave energy system using sequential Bayesian experimental design with machine learning techniques E Katsidoniotaki, S Guth, A Mojahed, M Göteman, T Sapsis | 4* | 2023 |
| Application of Gaussian process multi-fidelity optimal sampling to ship structural modeling S Guth, B Champenois, TP Sapsis 34th Symp. on Naval Hydrodynamics, Washington, DC, June, 2022 | 3 | 2022 |
| Probabilistic characterization of the effect of transient stochastic loads on the fatigue-crack nucleation time S Guth, TP Sapsis Probabilistic Engineering Mechanics 66, 103162, 2021 | 2 | 2021 |
| A stochastically preluded Karhunen-Loève representation for recovering extreme statistics in ship dynamics S Guth, TP Sapsis Proc. 1st Int. Conf. On Stability and Safety of Ships and Ocean Vehicles …, 2021 | 1 | 2021 |
| MICRODEM terrain grid computing: global SRTM geomorphometry PL Guth, SC Guth Computing, 1-6, 2006 | 1 | 2006 |
| Co-reasoning by Humans in the Loop as a Goal for Designers of Machine Learning-Driven Algorithms in Medicine S Guth The American Journal of Bioethics 24 (9), 120-122, 2024 | | 2024 |
| Analytical and computational methods for non-Gaussian reliability analysis of nonlinear systems operating in stochastic environments SC Guth Massachusetts Institute of Technology, 2023 | | 2023 |
| Likelihood-weighted active learning with application to Bayesian optimization and uncertainty quantification for complex fluid flows T Sapsis, A Blanchard, E Pickering, S Guth Bulletin of the American Physical Society 67, 2022 | | 2022 |
| Analytic Methods for Estimating the Effects of Stochastic Intermittent Loading on Fatigue-Crack Nucleation S Guth, T Sapsis Advances in Nonlinear Dynamics: Proceedings of the Second International …, 2021 | | 2021 |
| Active Search methods to predict material failure under intermittent loading in the Serebrinksy-Ortiz fatigue model S Guth, T Sapis Dynamic Data Driven Applications Systems: Third International Conference …, 2020 | | 2020 |
| An exploration of data-driven techniques for predicting extreme events in intermittent dynamical systems SC Guth Massachusetts Institute of Technology, 2019 | | 2019 |
Themistoklis SapsisProfessor of Mechanical and Ocean Engineering, MITAdresse e-mail validée de mit.edu
