Work

Embedded Model Form Uncertainty Quantification with Measurement Noise for Bayesian Model Calibration

Year: 2025

Type: article

Abstract: Implementation, data, results and manuscript for the generation of the paper "Embedded Model Form Uncertainty Quantification with Measurement Noise for Bayesian Model Calibration". Tu generate the results and the paper end-to-end, install the environment from code/env.yml and run from the code folder doit -f dodo_master.py. Different parts to be run can be modified by changing the parameters in the respective dodo files.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15720779

Author: Andrés Arcones, Daniel

Institution: Federal Institute For Materials Research and Testing

Language: English

Topic: Probabilistic and Robust Engineering Design

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

Funder: Deutsche Forschungsgemeinschaft

Award: G4528655387