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Abstract: Across scientific missions, we regularly need to develop accurate models that closely predict experimental observation.  Our team is developing a new class of model, called the learned predictive model, that captures theory-driven simulation, but also improves by exposure to experimental observation.  We begin by designing specialized deep neural networks that can learn the behavior of complicated simulation models from exceptionally large simulation databases.  Later, we improve, or elevate, the trained models by incorporating experimental data using a technique called transfer learning. The training and elevation process improves our predictive accuracy, provides a quantitative measure of uncertainty, and helps us cope with limited experimental data volumes.  To drive this procedure, we have also developed a complex computational workflow that can generate hundreds of thousands to billions of simulated training examples and can steer the subsequent training and elevation process. These workflow tasks require a heterogeneous high-performance computing environment supporting computation on CPUs, GPUs, and sometimes specialized, low-precision processors.  We will present a global view of our deep learning efforts, our computational workflows, and some implications that this computational work has for current and future large-scale computing platforms. 

July 9: Compressed sensing

for NMR (tentative)

Abstract: Current analyses of urban development use either simple, bottom-up models, that have limited predictive performance, or highly engineered, complex models relying on many sources of survey data that are typically scarce and difficult and expensive to collect. This talk presents work-in-progress developing a data-driven, flexible, non-parametric framework to simulate realistic urban forms using generative adversarial networks and planetary-level remote-sensing data. To train our urban simulator, we  curate and put forth a new dataset on urban form, integrating spatial distribution maps of population, nighttime luminosity, and built land densities, as well as best-available information on city administrative boundaries for 30,000 of the world's largest cities. This is the first analysis to date of urban form using modern generative models and remote-sensing data.