Abstract:

Creep is the slow, continuous deformation of a material under constant stress, especially at high temperatures. Accurately identifying its parameters remains a critical challenge in materials science. This work focuses on calibrating these parameters using a Bayesian calibration method via the Python package ACBICI. This approach provides uncertainty-aware and probabilistic calibration tools, overcoming the limitations of traditional deterministic methods. Results from the first two stages are analyzed through a series of tests, highlighting the advantages of the Bayesian framework. Future work includes extending the method to the third stage of creep and further developing and optimizing the ACBICI framework