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A Convergence of Complexities in Climate Systems and the Role of High-Performance Computing
DescriptionPredictive understanding and actionable insights in sustainability in the modern era requires an effective blend of theory and data-driven sciences. Relevant theory include physics, biogeochemistry, and ecology within the natural sciences, and engineering principles, economics, social, and governance principles in human-engineered systems and the social sciences. The data-driven sciences need to consider Big Data such as from archived numerical model simulations along with remotely sensed observations, and relatively small data such as from historical observations or even prehistorical proxy records, as well as based on prior domain knowledge and lessons learned from rare events and extremes. The underlying spatiotemporal data generation processes may be nonlinear dynamical, even chaotic, while the variability may be low frequency, even 1/f noise. Data may be sparse or incomplete, prior knowledge and physics may be incomplete or over-parameterized, while falsifiability and comprehensive uncertainty characterization are critical to inform decisions and add to our collective knowledge. Understanding the implications for domain-aware high performance computing may be critical both for the sciences and engineering and for investments or research directions in supercomputing. The first part of the presentation will describe these challenges and discuss how next-generation Artificial Intelligence may be able to provide solutions and where further developments may be necessary. The second part of the presentation will discuss recent research at my Sustainability and Data Sciences Laboratory, specifically, on the impacts of climate variability and weather extremes in ecology and biodiversity and in urban or regional critical lifeline infastructures, with an emphasis on the associated challenges and opportunities in processing earth science data.
LocationDallas Ballroom/Omni Hotel
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