Multi-Scale Morse Theory for Science Discovery

Effective use of data management techniques for massive scientific data are a crucial ingredient for the success of data intensive scientific investigation. Developing such techniques involves a number of major challenges such as the real-time management of large models, or the quantitative analysis of scientific features of unprecedented complexity. The research developed at the Center for Extreme Data Management Analysis and Visualization (CEDMAV) addresses these challenges with and interdisciplinary approach involving diverse topics including mathematical foundations of data representations, the design of robust, efficient algorithms, and the integration with relevant applications in physics, biology, or medicine.

In this talk, I will present the application of a discrete topological framework for the representation and analysis of large scale scientific data. Due to the combinatorial nature of this framework, we can implement the core constructs of Morse theory without the approximations and instabilities of classical numerical techniques. In particular, the inherent robustness of the approach allows to address the high complexity of the feature extraction problem for high resolution scientific data. Our approach has enabled the successful quantitative analysis for several massively parallel simulations including the study turbulent hydrodynamic instabilities, porous material under stress and failure, the energy transport of eddies in ocean data used for climate modeling, and lifted flames that lead to clean energy production.

During the talk, I will provide a live demonstration of the effectiveness of some software tools developed and discuss the deployment strategies in an increasing heterogeneous computing environment including DOE parallel supercomputers such as Titan or Mira.