Reduced Order Models of Fractured Systems using Graph Theory and Machine Learning

When
Start: 05/09/2017 - 11:00am
End  : 05/09/2017 - 12:00pm

Category
Applied Math Seminar

Speaker
Gowri Srinivasan (LANL)

Abstract

Microstructural information (fracture size, orientation, etc.) plays a key role in governing the dominant physics for two timely applications of interest to LANL: dynamic fracture processes like spall and fragmentation in metals (weapons performance) and detection of gas flow in static fractures in rock due to underground explosions (nuclear nonproliferation). Micro-fracture information is only known in a statistical sense, so representing millions of micro-fractures in 1000s of model runs to bound the uncertainty requires petabytes of information. Our critical advance is to integrate computational physics, machine learning and graph theory to make a paradigm shift from computationally intensive grid-based models to efficient graphs with at least 3 orders of magnitude speedup for Discrete Fracture Networks (DFNs).

Where
CGU Math South