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20212026
most citedA Bayesian neural network approach to Multi-fidelity surrogate modelling

9 citations · 9 across the 4 of their papers we have counts for

collaborators

5 papers

physics.data-an2026

Multi-fidelity Monte Carlo estimation of floor response spectra under combined seismic and structural parameter uncertainties

Nils Baillie, Baptiste Kerleguer, Cyril Feau +2

Floor response spectra (FRS) are essential tools for the design of non-structural elements (such as equipment or components). Given the various physical phenomena influencing FRS,…

stat.AP2025

Multi-fidelity Gaussian process regression for noisy outputs and non-nested experimental designs: a comparison between the recursive and non-recursive formulations

Nils Baillie, Baptiste Kerleguer, Cyril Feau +1

This paper investigates a recursive formulation of auto-regressive multi-fidelity Gaussian process regression in the challenging setting of noisy and non-nested high- and low-fidel…

stat.ME2024

Bayesian Calibration for Prediction in a Multi-Output Transposition Context

Charlie Sire, Josselin Garnier, Cédric Durantin +3

Numerical simulations are widely used to predict the behavior of physical systems, with Bayesian approaches being particularly well suited for this purpose. However, experimental o…

math.ST2023★ 9 cited

A Bayesian neural network approach to Multi-fidelity surrogate modelling

Baptiste Kerleguer, Claire Cannamela, Josselin Garnier

This paper deals with surrogate modelling of a computer code output in a hierarchical multi-fidelity context, i.e., when the output can be evaluated at different levels of accuracy…

math.ST2021

Multi-fidelity surrogate modeling for time-series outputs

Baptiste Kerleguer

This paper considers the surrogate modeling of a complex numerical code in a multifidelity framework when the code output is a time series. Using an experimental design of the low-…