Digital Image Correlation enriched quantification of polymorphic random field uncertainty in finite-element models KU Leuven
The objective of this PhD is to develop an inverse method for the quantification of imprecise random fields, based on full-field strain measurements (Digital Image Correlation (DIC)). A physics-informed deep-learning architecture (e.g., an intelligent combination of a convolutional/recurrent Neural Networks or deep Gaussian process with the governing physical equations) is trained to predict imprecise random-field descriptors (covariance ...