The investigator and her colleagues propose to organize a workshop to identify opportunities for future mathematical advances required for enabling scientific discoveries from petascale scientific data. The workshop will embrace scientists from academia, national laboratories, and industry. Application scientists, mathematicians, statisticians, high performance data miners will define a research agenda for developing the next-generation mathematical techniques needed to meet the challenges posed by petascale data sets. They will produce a workshop report that will: * Articulate the requirements of various scientific domains such as global carbon cycle modeling, fusion energy production, nationally security, etc. * Delineate appropriate mathematical approaches and techniques, * Determine the current state-of-the-art in these approaches and techniques, and * Identify the gaps that must be addressed to enable the effective analysis of large, complex data sets in the next five to ten years.
Renewable energy production, carbon sequestration, national security, and human health protection are the urgent issues in today's society. Major incentives-economic, geopolitical, and environmental-drive a scientific mandate to research and develop cost-effective and beneficial solutions. It is imperative to truly synthesize the three pillars of scientific discovery-experimentation, theory, and ultra-scale computation-to address these challenges effectively and comprehensively. Advances in each have been already revolutionizing the way science is conducted. With this promise, however, comes a problem - the massive quantities of data so produced by national high-throughput experimental faculties, observatories and ultrascale computing facilities. Those data hold the answers to fundamental questions about the nature of the universe. However, the answers will be subtly hidden in the raw data. Those data need to be analyzed to extract knowledge - to understand the science. Discovering such new knowledge will require the next-generation mathematical techniques from several fields, including but not restricted to statistics, machine learning, image analysis, and pattern recognition. Advances in these areas will also enable iteratively validating ultra-scale simulations with experimental and observational data. As a result, these technologies will bring revolutionary and unconventional solutions to some of our most pressing and expensive challenges in health, energy, environment, and national security.
