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20172022
most citedA spectral surrogate model for stochastic simulators computed from trajectory samples

31 citations · 31 across the 1 of their papers we have counts for

collaborators

6 papers

stat.CO2022★ 31 cited

A spectral surrogate model for stochastic simulators computed from trajectory samples

Nora Lüthen, Stefano Marelli, Bruno Sudret

Stochastic simulators are non-deterministic computer models which provide a different response each time they are run, even when the input parameters are held at fixed values. They…

stat.CO2021

Global sensitivity analysis using derivative-based sparse Poincaré chaos expansions

Nora Lüthen, Olivier Roustant, Fabrice Gamboa +3

Variance-based global sensitivity analysis, in particular Sobol' analysis, is widely used for determining the importance of input variables to a computational model. Sobol' indices…

math.NA2020

Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark

Nora Lüthen, Stefano Marelli, Bruno Sudret

Sparse polynomial chaos expansions (PCE) are a popular surrogate modelling method that takes advantage of the properties of PCE, the sparsity-of-effects principle, and powerful spa…

physics.comp-ph2019

Geometry of martensite needles in shape memory alloys

Sergio Conti, Martin Lenz, Nora Lüthen +2

We study the geometry of needle-shaped domains in shape-memory alloys. Needle-shaped domains are ubiquitously found in martensites around macroscopic interfaces between regions whi…

physics.flu-dyn2019

Optimal sensor placement for artificial swimmers

Siddhartha Verma, Costas Papadimitriou, Nora Luethen +2

Natural swimmers rely for their survival on sensors that gather information from the environment and guide their actions. The spatial organization of these sensors, such as the vis…

math.NA2017

Branching Structures in Elastic Shape Optimization

Nora Lüthen, Martin Rumpf, Sascha Tölkes +1

Fine scale elastic structures are widespread in nature, for instances in plants or bones, whenever stiffness and low weight are required. These patterns frequently refine towards a…