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.

 

 

 

Data-driven Coarse-Graining: Comparison of the evolution of the density of 2400 walkers over 20000 time steps (for two different initializations) with the probabilistic, coarse-grained evolution law learned using data over 400 time steps. The fine-scale interactions are such that the density (in the limit) evolves according to an advection-diffusion equation.