2019-2020 Assessment of the Army Research Laboratory Interim Report (2020) / Chapter Skim
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3 Computational and Atmospheric Sciences
3 Computational and Atmospheric Sciences
Pages 24-37
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From page 24... ...
In addition, the work on ML to characterize particle shapes using scattered light images has the potential for wide applicability throughout aerosol science and in the broad area of chemical or biological agent characterization. There are a few areas of opportunity.
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From page 25... ...
In turn, computational science is becoming a prominent element in training AI/ML systems, such as work using physics-based simulation engines from video games as the source of data to train autonomous vehicles. Further, many of the technical challenges in modern ML involve problems that have been well investigated in computational science work, and emerging research in academia and industry that involves substantive collaborations between computational science and AI/ML is increasingly seen.
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From page 26... ...
In addition to these important advancements on existing ARL multiscale computational technology, the HMS project team is publishing results regularly in high-quality journals and has added a university collaborator to provide additional expertise on uncertainty quantification for the project. As such, this project includes a broad set of key elements of research quality, including highquality publications, external collaboration, and applications to design, analysis, discovery, and uncertainty quantification.
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From page 27... ...
Each is a form of computational simulation, each requires careful consideration of accuracy and uncertainty for an intended use, and each requires due attention to performance and scalability in mission settings. Depth Versus Breadth As an enabling, broad-based capability, the computational sciences need to maintain a critical mass of expertise, both for targeted projects and for collaborative engagement with other projects.
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From page 28... ...
Further, many of the technical challenges in modern machine learning involve problems that have been well investigated in computational science work, and emerging research in academia and industry that involves substantive collaborations between computational science and AI/ML is increasingly seen. CISD is poised to catalyze important new work that involves collaborations between computational and information sciences, an area that does not currently appear to be pursued.
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From page 29... ...
influence of a forest canopy on flows in complex terrain; (3) improving numerical weather prediction in convectively unstable environments by assimilating radar observations; (4)
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From page 30... ...
Simulating Turbulent, Natural Convection Using the Vortex Filament Method This is an update to a project first described to the ARLTAB in 2017. An idealized Lagrangian vorticity model is used to explore new ways of representing turbulence in the presence of localized heating.
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From page 31... ...
Additional work using the faculty, students, and facilities at New Mexico State University is being planned, and the group is aware of other atmospheric UAS efforts.7 Aircraft Vortex Detection and Characterization: C-17 Formation Spacing Research Project The C-17 is one of the "workhorses" involved in airdrop operations supporting the Army. Existing standards for wake turbulence mandate a standard separation between aircraft, and the spacing limits the speed with which multiple drops of equipment and personnel can be accomplished.
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From page 32... ...
Application of Machine Learning to Characterization of Particle Shape Using Scattered Light Images The characterization of particle shape helps to identify classes of particles that may be present in an environment. Imaging techniques that can be used to help to identify particle shapes are widely available (e.g., scanning electron microscopy analyses)
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From page 33... ...
It is worth restating that, while full BED collaboration with other laboratory projects has the potential for great impact on these other projects, it will put a significant strain on BED's existing resources. Turbulence Modeling in Urban, Complex, and Forested Domains Using the Lattice-Boltzmann Method Although the work is sound and has clear implications for supporting future Army operations in challenging operational environments, some important issues can be raised about the future of this effort.
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From page 34... ...
Application of Machine Learning to Characterization of Particle Shape Using Scattered Light Images The potential that this work provides for enhancing Army research and broader impacts in the scientific community is exceptionally high. Of particular note is the potential to enable the identification of multicomponent materials.
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From page 35... ...
The research teams are composed of high-quality personnel with relevant subject matter expertise for the associated projects. The team personnel reflect a good mix of junior, mid-career, and senior staff, and the research outcomes appear to be both ambitious and feasible The computational sciences activities are very well supported through the ARL Supercomputing Research Center, which includes both a DoD Supercomputing Research Center and a DoD Data Analysis and Assessment Center.
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From page 36... ...
The research projects presented employ the appropriate theoretical and computational methods, and while several projects involving experimental validation are just now beginning to utilize observational data, the necessary plans are in place to move those forward. Those using existing sensors, such as the radar assimilation project described earlier, have generated data sets appropriate to the project's needs.
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From page 37... ...
Thus, strategic integration of BED research thrusts with complementary research in other areas is encouraged. One mechanism for accomplishing this is the use of the network and information sciences and computational sciences taxonomy to optimize the impact of the BED expertise and limited resources, while providing opportunities for critical research in environmental research and across disciplines.
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