Dr. Christina Schenk of IMDEA Materials Institute presented her latest research at the XI International Conference on Coupled Problems in Science and Engineering (Coupled Problems 2025), held in Villasimius, Sardinia.
Her talk, titled “ACBICI: Enhancing Bayesian Calibration for Computationally Intensive and Uncertain Systems”, took place on Wednesday, 28 May, during the special session on Data-driven multiscale modelling and machine learning for biomedical, physical, engineering, and social coupled systems.
In her presentation, Dr. Schenk introduced ACBICI, a Python library developed to extend the classical Bayesian calibration framework proposed by Kennedy & O’Hagan. The tool leverages Gaussian Process (GP) surrogates to enable efficient model calibration and robust uncertainty quantification, critical capabilities in fields where data is limited and simulations are computationally expensive.
Her talk highlighted the relevance of such tools to industrial and biomedical domains, including case studies on creep behaviour in steels and tumour growth modelling in glioblastoma. These examples underscore ACBICI’s potential to support high-fidelity predictive modelling in data-scarce environments, such as those addressed in the AID4GREENEST project.
As a member of the AID4GREENEST consortium, Dr. Schenk’s participation reflects the project’s commitment to integrating advanced AI-based modelling approaches to tackle challenges in the steel sector, including material design and performance prediction under uncertainty.