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The unique challenges associated with imaging a black hole motivated the development of new computational imaging algorithms. As the Event Horizon Telescope continues to expand, these algorithms will need to evolve to keep pace with the increasingly demanding volume and dimensionality of the data.
Research software infrastructure is critical for accelerating science, and yet, these digital public goods are often unsustainably funded. Solving this problem requires an appreciation of the intrinsic value of research software outputs, and greater investment of time and effort into effectively funding maintenance of software at scale.
To best learn from data about large-scale complex systems, physics-based models representing the laws of nature must be integrated into the learning process. Inverse theory provides a crucial perspective for addressing the challenges of ill-posedness, uncertainty, nonlinearity and under-sampling.
Emerging exascale architectures and systems will provide a sizable increase in raw computing power for science. To ensure the full potential of these new and diverse architectures, as well as the longevity and sustainability of science applications, we need to embrace software ecosystems as first-class citizens.
Software is essential to computational science research, and yet it hasn’t achieved first-class status when it comes to citations. It’s time for all of us in the research community to change this behavior.