and the 2013 discovery of the Higgs boson),
powerful astronomy instruments
(such as the Hubble Space Telescope,
which yielded insights into the universe’s
expansion and dark energy), or
high-throughput DNA sequencers and
exploration of metagenomics ecology,
ever-more powerful scientific instruments
continually advance knowledge.
Each such scientific instrument,
as well as a host of others, is critically
dependent on computing for sensor
control, data processing, international
collaboration, and access.
However, computing is much more
than an augmenter of science. Unlike
other tools, which are limited to particular
scientific domains, computational
modeling and data analytics are applicable
to all areas of science and engineering,
as they breathe life into the
underlying mathematics of scientific
models. They enable researchers to understand
nuanced predictions, as well
as shape experiments more efficiently.
They also help capture and analyze the
torrent of experimental data being produced
by a new generation of scientific
instruments made possible by advances
in computing and microelectronics.
Computational modeling can illuminate
the subtleties of complex
mathematical models and advance
science and engineering where time,
cost, or safety precludes experimental
assessment alone. Computational
models of astrophysical phenomena,
on temporal and spatial scales as di-