Resumen:

Creep is the slow, continuous deformation of a material when
it is subjected to constant stress over time, especially at high
temperatures. The accurate identification of its parameters remains a
critical challenge in materials science. This research focuses on
calibrating these parameters using the Bayesian calibration method,
specifically through a Python package called ACBICI. The motivation for
this methodology comes from the need for uncertainty-aware and
probabilistic calibration tools that go beyond the limitations of
conventional deterministic methods. The calibration results of the first
two stages are analyzed through a series of tests, and the advantages of
the Bayesian framework are discussed. Future work includes extending the
approach to the third stage of creep, as well as further development and
optimization of the ACBICI framework.