The ultimate goal of the DARPA Accelerated Computation for Efficient Scientific Simulation (ACCESS) is to demonstrate new, specialized benchtop technology that can solve large problems in complex physical systems on the hour timescale, compared to existing methods that require full cluster-scale supercomputing resources and take weeks to months. The core principle of the program is to leverage advances in optics, MEMS, additive manufacturing, and other emerging technologies to develop new non-traditional hybrid analog and digital computational means.
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System-of-Systems (SoS) architectures are increasingly central in managing
defense, national security and urban infrastructure applications. However, it
is difficult to model and currently impossible to systematically design such
complex systems using existing tools, which has led to inferior performance,
unexpected problems and weak resilience.
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Complex physical systems, devices and processes important to the Department of Defense (DoD) are often poorly understood due to uncertainty in models, parameters, operating environments and measurements. The goal of DARPA’s Enabling Quantification of Uncertainty in Physical Systems (EQUiPS) program is to provide a rigorous mathematical framework and advanced tools for propagating and managing uncertainty in the modeling and design of complex physical and engineering systems. Of particular interest to the program are systems with multi-scale coupled physics and uncertain parameters in extremely high-dimensional spaces, such as new aerospace vehicles and engines.
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The goal of the EXTREME Program is to develop new optical components, devices, systems, architectures and design tools using Engineered Optical Materials (EnMats) to enable new functionality and/or vastly improve size, weight, and power characteristics of traditional optical systems. EnMats are broadly defined to include, but are not limited to, metamaterials (both metallic and dielectric), scattering surfaces and volumes, holographic structures, and diffractive elements.
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The science of network analysis is in its infancy. Currently, the structure of real-world networks is described only in terms of coarse and basic details such as diameter, degree distribution, etc. In addition, as networks become large, many problems are intractable as the classical algorithms for these problems run in exponential time with respect to the size of the graph. A large number of important problems (e.g., structural and functional brain dynamics or gene-protein and disease networks) can be formulated as graph problems. A comprehensive mathematical understanding of large networks is needed in order to effectively apply and scale graph-based network analysis techniques for use in DoD-relevant scenarios.
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