Seminar "An Information Theoretic Approach towards Predictive Materials Modeling"

Predictive modeling and design of materials gives rise to unique mathematical and computational challenges not present in other areas of physical sciences where modeling in the presence of uncertainties is important. Such challenges include but not limited to (i) Modeling of hierarchical random heterogeneous material structures; (ii) Propagating uncertainties in a quantifiable manner across spatial and temporal length scales (stochastic coarse graining); (iii) Addressing the curse of stochastic dimensionality; (iv) The limitations of phenomenology typical in most materials science models; (v) Modeling failure and rare events in random media; and many more.   

We will briefly address these challenges in predictive modeling of heterogeneous media and discuss data-driven models of material structure, the curse of stochastic dimensionality in forward uncertainty propagation, stochastic coarse graining, development of inexpensive surrogate stochastic models and posing multiscale problems in a graph theoretic framework.