123 citations · 242 across the 7 of their papers we have counts for
7 papers
Nonlinear dimension reduction for surrogate modeling using gradient information
Daniele Bigoni, Youssef Marzouk, Clémentine Prieur +1
We introduce a method for the nonlinear dimension reduction of a high-dimensional function , . Our objective is to identify a nonlinear…
Data-Driven Forward Discretizations for Bayesian Inversion
Daniele Bigoni, Yuming Chen, Nicolas Garcia Trillos +2
This paper suggests a framework for the learning of discretizations of expensive forward models in Bayesian inverse problems. The main idea is to incorporate the parameters governi…
Greedy inference with structure-exploiting lazy maps
Michael C. Brennan, Daniele Bigoni, Olivier Zahm +2
We propose a framework for solving high-dimensional Bayesian inference problems using \emph{structure-exploiting} low-dimensional transport maps or flows. These maps are confined t…
Inference via low-dimensional couplings
Alessio Spantini, Daniele Bigoni, Youssef Marzouk
We investigate the low-dimensional structure of deterministic transformations between random variables, i.e., transport maps between probability measures. In the context of statist…
A Stabilised Nodal Spectral Element Method for Fully Nonlinear Water Waves
Allan Peter Engsig-Karup, Claes Eskilsson, Daniele Bigoni
We present an arbitrary-order spectral element method for general-purpose simulation of non-overturning water waves, described by fully nonlinear potential theory. The method can b…
Efficient uncertainty quantification of a fully nonlinear and dispersive water wave model with random inputs
Daniele Bigoni, Allan P. Engsig-Karup, Claes Eskilsson
A major challenge in next-generation industrial applications is to improve numerical analysis by quantifying uncertainties in predictions. In this work we present a formulation of…
Spectral tensor-train decomposition
Daniele Bigoni, Allan P. Engsig-Karup, Youssef M. Marzouk
The accurate approximation of high-dimensional functions is an essential task in uncertainty quantification and many other fields. We propose a new function approximation scheme ba…