Defense Advanced Research Projects AgencyTagged Content List

Algorithms

A process or rule set used for calculations or other problem-solving operations

Showing 65 results for Algorithms RSS
August 29, 2017,
Webcast
The Defense Advanced Research Projects Agency (DARPA) Defense Sciences Office (DSO) is sponsoring a Proposers Day webcast to provide information to potential proposers on the objectives of an anticipated Research Announcement (RA) for the Young Faculty Award (YFA) program.
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.
The A-to-I Look-Through Program will fundamentally improve the operational bandwidth, linearity, and efficiency of electronic systems where the objective is to receive and transmit information using electromagnetic (radio) waves under extreme size/weight/power and environmental conditions required for DoD applications. This will be achieved by developing new electronic processing subsystems methods and architectures based on new understandings of mathematical principles and embedded signal processing. This program will develop ultra-wideband digital RF receivers based on A-to-I converter (AIC) technology.
The Artificial Intelligence Research Associate (AIRA) program is part of a broad DAPRA initiative to develop and apply “Third Wave” AI technologies that are robust to sparse data and adversarial spoofing, and that incorporate domain-relevant knowledge through generative contextual and explanatory models.
Deep Purple aims to advance the modeling of complex dynamic systems using new information-efficient approaches that make optimal use of data and known physics at multiple scales. The program is investigating next-generation deep learning approaches that use not only high throughput multimodal scientific data from observations and controlled experiments (including behaviors such as phase transitions and chaos), but also of the known science of such systems at whatever scales it exists.