3 papers
physics.flu-dyn2021
Statistical Learning for Fluid Flows: Sparse Fourier divergence-free approximations
Luis Espath, Dmitry Kabanov, Jonas Kiessling +1
We reconstruct the velocity field of incompressible flows given a finite set of measurements. For the spatial approximation, we introduce the Sparse Fourier divergence-free (SFdf)…
math.NA2018
Multilevel Double Loop Monte Carlo and Stochastic Collocation Methods with Importance Sampling for Bayesian Optimal Experimental Design
Joakim Beck, Ben Mansour Dia, Luis F. R. Espath +1
An optimal experimental set-up maximizes the value of data for statistical inferences and predictions. The efficiency of strategies for finding optimal experimental set-ups is part…
math.NA2018
Nesterov-aided Stochastic Gradient Methods using Laplace Approximation for Bayesian Design Optimization
Andre Gustavo Carlon, Ben Mansour Dia, Luis FR Espath +2
Finding the best setup for experiments is the primary concern for Optimal Experimental Design (OED). Here, we focus on the Bayesian experimental design problem of finding the setup…