Welcome to the Continuum Mechanics Group

We strongly believe that the rapid advances in data sciences and machine learning will not and should not leave physical modeling untouched. The group promotes a data-driven perspective for carrying out engineering analysis and design tasks, in calibrating and validating computational models, but also in discovering new ones that are predictive of multiscale behavior. We employ a probabilistic-Bayesian mindset  to achieve the fusion of data and models and attempt to address fundamental challenges relating to dimensionality & model reduction and the discovery of salient patterns in the presence of small data.

 

 

 

Graphical illustration of an integrated, three-step process to coarse-graining. Beginning from the top left: For the given microstructure x , a low-dimensional latent space representation X is found via the encoding density p_c. In the next step, X serves as the input to a coarse-grained model (CGM) based on simplified physics and/or coarser spatial resolution. The CGM output Y(X) is then used to reconstruct the fine-grained model's solution using y using the decoding density p_cf.