Ensemble Kalman Filtering for Stochastic Phase Field Models

Explore how Ensemble Kalman Filtering addresses uncertainty in stochastic phase field models for brittle fracture simulation in this 16-minute conference talk from FerriteCon 2025. Learn to combine phase field models that represent evolving cracks as continuous damage variables with Ensemble Kalman Filtering techniques to quantify and reduce uncertainty in mechanical system simulations. Discover how treating model parameters and simulation states as random variables enables the Ensemble Kalman Filter to use simulated ensembles and available data to update predictions and improve the reliability of fracture forecasts. Understand the practical applications of this approach for handling inevitable uncertainties about material parameters or conditions that can significantly affect simulation predictions in complex mechanical systems. Gain insights into advanced numerical methods within Julia's ecosystem for scientific computing, stochastic modeling, and uncertainty quantification, making this presentation valuable for researchers and practitioners working with computational mechanics and probabilistic modeling approaches.