The state-of-the-art of computational modeling and simulation is anything but static. Tremendous progress has been achieved in the ability to model and predict complex behavior in multi-scale, multi-physics systems and materials. New questions can now be asked through our simulations, such as how sensitive are the quantities of interest to important input parameters, what is the uncertainty in the results, how credible are the answers, how can the models be improved, and how can simulations support critical design, economic, or safety decisions? Such progress is bound to continue, but it will require an expanded community of experimentalists, computer scientists, statisticians, and mathematicians. Moreover, the push to exascale computing is placing new demands on our algorithms, application software, programming models, and analysis workflows. Big data, and big data problems, are everywhere. This talk will delve into these questions, and discuss new research intended to address some of the challenges in the areas of uncertainty quantification, design optimization, and data assimilation.
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