  The use of computational simulation is by now so pervasive in society that it
is no exaggeration to say that continued U.S. and international prosperity,
security, and health depend in part on continued improvements in simulation
capabilities. What if we could predict weather two weeks out, guide the design
of new drugs for new viral diseases, or manage new manufacturing processes that
cut production costs and times by an order of magnitude? What if we could
predict collective human behavior, for example, response to an evacuation
request during a natural disaster, or labor response to fiscal stimulus? (See
also the companion CCC Quad Paper on Pandemic Informatics, which discusses
features that would be essential to solving large-scale problems like
preparation for, and response to, the inevitable next pandemic.)
  The past decade has brought remarkable advances in complementary areas: in
sensors, which can now capture enormous amounts of data about the world, and in
AI methods capable of learning to extract predictive patterns from those data.
These advances may lead to a new era in computational simulation, in which
sensors of many kinds are used to produce vast quantities of data, AI methods
identify patterns in those data, and new AI-driven simulators combine
machine-learned and mathematical rules to make accurate and actionable
predictions. At the same time, there are new challenges -- computers in some
important regards are no longer getting faster, and in some areas we are
reaching the limits of mathematical understanding, or at least of our ability
to translate mathematical understanding into efficient simulation. In this
paper, we lay out some themes that we envision forming part of a cohesive,
multi-disciplinary, and application-inspired research agenda on AI-driven
simulators.
