Defense Advanced Research Projects AgencyTagged Content List

Algorithms

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

Showing 14 results for Algorithms + Math RSS
03/19/2015
Whether designed to predict the spread of an epidemic, understand the potential impacts of climate change, or model the acoustical signature of a newly designed ship hull, computer simulations are an essential tool of scientific discovery. By using mathematical models that capture the complex physical phenomena of the real world, scientists and engineers can validate theories and explore system dynamics that are too costly to test experimentally and too complicated to analyze theoretically.
04/22/2016
Advanced materials are increasingly embodying counterintuitive properties, such as extreme strength and super lightness, while additive manufacturing and other new technologies are vastly improving the ability to fashion these novel materials into shapes that would previously have been extremely costly or even impossible to create. Generating new designs that fully exploit these properties, however, has proven extremely challenging.
05/06/2016
Whether designed to predict the spread of an epidemic, understand the potential impacts of climate change, or model the acoustical signature of a newly designed ship hull, computer simulations are an essential tool of scientific discovery. By using mathematical models that capture the complex physical phenomena of the real world, scientists and engineers can validate theories and explore system dynamics that are too costly to test experimentally and too complicated to analyze theoretically. Over the past half century, as supercomputers got faster and more powerful, such simulations became ever more accurate and useful.
05/26/2016
It’s not easy to put the intelligence in artificial intelligence. Current machine learning techniques generally rely on huge amounts of training data, vast computational resources, and a time-consuming trial and error methodology. Even then, the process typically results in learned concepts that aren’t easily generalized to solve related problems or that can’t be leveraged to learn more complex concepts. The process of advancing machine learning could no doubt go more efficiently—but how much so?
Machine learning has shown remarkable success across many application areas in recent years, leveraging advances in computing power and the availability of large sets of training data. It provides a tremendous opportunity to deploy data-driven systems in more complex and interactive tasks including personalized autonomy, agile robotics, self-driving vehicles, and smart cities. Despite dramatic progress, the machine learning community still lacks an understanding of the trade-offs and mathematical limitations of related technologies for a given domain, problem, or dataset.