activity
20152023
most citedBayesian inference and model comparison for metallic fatigue data

66 citations · 122 across the 6 of their papers we have counts for

collaborators

6 papers

stat.CO2023★ 1 cited

Modeling metallic fatigue data using the Birnbaum--Saunders distribution

Zaid Sawlan, Marco Scavino, Raul Tempone

This work employs the Birnbaum--Saunders distribution to model the fatigue life of metallic materials under cyclic loading and compares it with the normal distribution. Fatigue-lim…

stat.ME2018★ 8 cited

Spatial Poisson processes for fatigue crack initiation

Ivo Babuska, Zaid Sawlan, Marco Scavino +2

In this work we propose a stochastic model for estimating the occurrence of crack initiations on the surface of metallic specimens in fatigue problems that can be applied to a gene…

stat.CO2017★ 13 cited

Ensemble-marginalized Kalman filter for linear time-dependent PDEs with noisy boundary conditions: Application to heat transfer in building walls

Marco Iglesias, Zaid Sawlan, Marco Scavino +2

In this work, we present the ensemble-marginalized Kalman filter (EnMKF), a sequential algorithm analogous to our previously proposed approach [1,2], for estimating the state and p…

stat.AP2016★ 23 cited

Bayesian inferences of the thermal properties of a wall using temperature and heat flux measurements

Marco Iglesias, Zaid Sawlan, Marco Scavino +2

The assessment of the thermal properties of walls is essential for accurate building energy simulations that are needed to make effective energy-saving policies. These properties a…

stat.CO2015★ 66 cited

Bayesian inference and model comparison for metallic fatigue data

Ivo Babuska, Zaid Sawlan, Marco Scavino +2

In this work, we present a statistical treatment of stress-life (S-N) data drawn from a collection of records of fatigue experiments that were performed on 75S-T6 aluminum alloys.…

stat.ME2015★ 11 cited

A hierarchical Bayesian setting for an inverse problem in linear parabolic PDEs with noisy boundary conditions

Fabrizio Ruggeri, Zaid Sawlan, Marco Scavino +1

In this work we develop a Bayesian setting to infer unknown parameters in initial-boundary value problems related to linear parabolic partial differential equations. We realistical…