Daniele Bigoni
Daniele Bigoni
Post doctoral Associate at Massachusetts Institute of Technology
Verified email at mit.edu - Homepage
Cited by
Cited by
Inference via low-dimensional couplings
A Spantini, D Bigoni, Y Marzouk
The Journal of Machine Learning Research 19 (1), 2639-2709, 2018
Spectral tensor-train decomposition
D Bigoni, AP Engsig-Karup, YM Marzouk
SIAM Journal on Scientific Computing 38 (4), A2405-A2439, 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
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
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
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
Greedy inference with layers of lazy maps
D Bigoni, O Zahm, A Spantini, Y Marzouk
arXiv preprint arXiv:1906.00031, 2019
Uncertainty quantification with applications to engineering problems
D Bigoni
Technical University of Denmark, 2015
Unstructured spectral element model for dispersive and nonlinear wave propagation
AP Engsig-Karup, C Eskilsson, D Bigoni
The 26th International Ocean and Polar Engineering Conference, 2016
On the computation of monotone transports
D Bigoni, A Spantini, Y Marzouk
preparation, 2019
Comparison of classical and modern uncertainty quantification 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 anomalies, 2012
Curving Dynamics in High Speed Trains
D Bigoni
Technical University of Denmark, DTU Informatics, Kgs. Lyngby, Denmark, 2011
Variational inference via decomposable transports: algorithms for Bayesian filtering and smoothing
A Spantini, D Bigoni, YM Marzouk
Proceedings of the 30th Conference on Neural Information Processing Systems, 2016
Adaptive construction of measure transports for Bayesian inference
D Bigoni, A Spantini, Y Marzouk
NIPS workshop on Approximate Inference, 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
Modern uncertainty quantification methods in railroad vehicle dynamics
D Bigoni, AP Engsig-Karup, H True
Rail Transportation Division Conference 56116, V001T01A009, 2013
Data-driven forward discretizations for Bayesian inversion
D Bigoni, Y Chen, NG Trillos, Y Marzouk, D Sanz-Alonso
arXiv preprint arXiv:2003.07991, 2020
Adaptive spectral tensor-train decomposition for the construction of surrogate models
D Bigoni, AP Engsig-Karup, YM Marzouk
SIAM Conference on Computational Science and Engineering (SIAM CSE 2015), 2015
Anwendung der „Uncertainty Cluantification" bei eisenbahndynamischen Problemen
MSD Bigoni, AP Engsig-Karup
Application and Comparison of Uncertainty Quantification Methods for Railway Vehicle Dynamics with Random Mechanical Parameters
D Zhang, XU Peijuan, D Bigoni
Mechanics 25 (6), 455-462, 2019
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