| Inference via low-dimensional couplings A Spantini, D Bigoni, Y Marzouk Journal of Machine Learning Research 19 (66), 1-71, 2018 | 164 | 2018 |
| Spectral tensor-train decomposition D Bigoni, AP Engsig-Karup, YM Marzouk SIAM Journal on Scientific Computing 38 (4), A2405-A2439, 2016 | 164 | 2016 |
| A stabilised nodal spectral element method for fully nonlinear water waves AP Engsig-Karup, C Eskilsson, D Bigoni Journal of Computational Physics 318, 1-21, 2016 | 86 | 2016 |
| Greedy inference with structure-exploiting lazy maps M Brennan, D Bigoni, O Zahm, A Spantini, Y Marzouk Advances in Neural Information Processing Systems 33, 8330-8342, 2020 | 60 | 2020 |
| Sensitivity analysis of the critical speed in railway vehicle dynamics D Bigoni, H True, AP Engsig-Karup Vehicle System Dynamics 52 (sup1), 272-286, 2014 | 38 | 2014 |
| Nonlinear dimension reduction for surrogate modeling using gradient information D Bigoni, Y Marzouk, C Prieur, O Zahm Information and Inference: A Journal of the IMA 11 (4), 1597-1639, 2022 | 35 | 2022 |
| On the numerical and computational aspects of non-smoothnesses that occur in railway vehicle dynamics H True, AP Engsig-Karup, D Bigoni Mathematics and Computers in Simulation 95, 78-97, 2014 | 34 | 2014 |
| Uncertainty quantification with applications to engineering problems D Bigoni Technical University of Denmark, 2015 | 31 | 2015 |
| Efficient uncertainty quantification of a fully nonlinear and dispersive water wave model with random inputs D Bigoni, AP Engsig-Karup, C Eskilsson Journal of Engineering Mathematics 101 (1), 87-113, 2016 | 23 | 2016 |
| Greedy inference with layers of lazy maps D Bigoni, O Zahm, A Spantini, Y Marzouk arXiv preprint arXiv:1906.00031, 2019 | 17 | 2019 |
| Data-driven forward discretizations for Bayesian inversion D Bigoni, Y Chen, NG Trillos, Y Marzouk, D Sanz-Alonso Inverse Problems 36 (10), 105008, 2020 | 16 | 2020 |
| Adaptive construction of measure transports for Bayesian inference D Bigoni, A Spantini, Y Marzouk NIPS workshop on Approximate Inference, 2016 | 9 | 2016 |
| Comparison of classical and modern uncertainty qualification methods for the calculation of critical speeds in railway vehicle dynamics D Bigoni, AP Engsig-Karup, H True 13th Mini Conference on Vehicle System dynamics, Identification and Anomalities, 2012 | 8 | 2012 |
| On the computation of monotone transports D Bigoni, A Spantini, Y Marzouk preparation, 2019 | 7 | 2019 |
| Unstructured spectral element model for dispersive and nonlinear wave propagation AP Engsig-Karup, C Eskilsson, D Bigoni ISOPE International Ocean and Polar Engineering Conference, ISOPE-I-16-455, 2016 | 6 | 2016 |
| Global sensitivity analysis of railway vehicle dynamics on curved tracks D Bigoni, AP Engisg-Karup, H True Engineering Systems Design and Analysis 45844, V002T07A023, 2014 | 6 | 2014 |
| Modern uncertainty quantification methods in railroad vehicle dynamics D Bigoni, AP Engsig-Karup, H True Rail Transportation Division Conference 56116, V001T01A009, 2013 | 6 | 2013 |
| TransportMaps RM Baptista, D Bigoni, R Morrison, A Spantini MIT Uncertainty Quantification Group 2018, 123-124, 2015 | 5 | 2015 |
| Curving Dynamics in High Speed Trains D Bigoni Technical University of Denmark, DTU Informatics, Kgs. Lyngby, Denmark, 2011 | 5 | 2011 |
| DisruptionBench and Complimentary New Models: Two Advancements in Machine Learning Driven Disruption Prediction L Spangher, M Bonotto, W Arnold, D Chayapathy, T Gallingani, ... Journal of Fusion Energy 44 (1), 26, 2025 | 3 | 2025 |
Youssef MarzoukProfessor, Massachusetts Institute of TechnologyVerified email at mit.edu
