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Many cities are vulnerable to disaster-related mortality and economic loss. Smart City Digital Twins can be used to facilitate disaster decision-making and influence policy, but first they must accurately capture, predict, and adapt to the city’s dynamics, including the varying pace at which changes unfold.
Gravitational-wave discoveries have ignited a new era of astronomy. Numerical relativity plays a crucial role in modeling gravitational-wave sources for current and next-generation observatories, but it doesn’t come without computational challenges.
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.
Over the past decade, the materials science community has fostered the development of materials databases from high-performance computation. While these databases have achieved great success, there are still several challenges to be addressed for the community to realize the full potential of the materials-by-design era.
COVID-19 models have been extensively used to inform public health officials about potential interventions. Nevertheless, careful attention must be taken when extrapolating projections and parameters across different regions, as there is no one-size-fits-all model for the pandemic.
Detailed, accurate data related to a disease outbreak enable informed public health decision making. Given the variety of data types available across different regions, global data curation and standardization efforts are essential to guarantee rapid data integration and dissemination in times of a pandemic.
