Engineering Sciences Basic Research Portfolio

Mechanical Sciences Portfolio: Includes research supported in the Mechanical Sciences Division, which is concerned with a broad spectrum of fundamental investigations in the disciplines of fluid dynamics, solid mechanics, structures and dynamics, and propulsion and energetics. Although many creative and imaginative studies concentrate on a particular subdiscipline, there are new contributions arising from interdisciplinary approaches such as the coupling between aerodynamics and structures; combustion and fluid dynamics; or solid mechanics and structures as seen in the structural reliability areas. Active controls and computational multiscale mechanics are two examples of the common themes that run through these four subdisciplines. Research in these areas is addressed within the context of the application rather than as a separate subject of study.

Electronics Portfolio: Supports scientific and engineering research endeavors that could potentially define new electronic-photonic-magnetic capabilities as well as enhance future Soldier electronic performance. Recently supported research under electronics can be divided into five application areas: Multimodal Sensing, Ubiquitous Communications, Intelligent Information Technology, Power Electronics, and Electromagnetic Warfare. The Electronics research subareas are sensors and detectors; solid state devices; optoelectronics; quantum electromagnetic devices; power electronics; electromagnetics and radio frequency circuit integration; and terahertz science and technology.

Environmental Sciences Portfolio: Principally investigates fundamental phenomenology of the atmospheric lower boundary layer and the terrestrial surface and near-surface environments to ensure the ability of the Army to function as the world's premier terrestrial element of military power. In addition, Environmental Sciences examines fundamental basic research related to environmental quality, particularly the remediation, restoration, and sustainability of military lands.

Material Sciences Portfolio: Supports the overall goal of the Materials Science Division, which is to elucidate the fundamental relationships that link the composition, microstructure, defect structures, processing, and properties of materials in order to realize unprecedented materials properties. This knowledge enables the research program to continue to generate the new discoveries that will permit the Army to maintain the overall technological edge required for the future.

Mathematical and Information Sciences Basic Research Portfolio

Computing and Information Sciences: Research is required in broad thrust areas including end-to-end admission; flow and congestion control; adaptive routing and channel access protocols; adaptive transmission techniques for power and spectral efficiency; signal processing for robust communications; intelligent control of autonomous systems and agents; robust non-linear estimation and control; novel sensor and control applications; improved methods for optimization of complex non-linear systems; distributed and embedded systems theory; information processing and fusion; highly assured, intrusion tolerant, survivable information systems; high confidence software and malware detection; and security for Army mobile and fixed-based communications.

Mathematics Portfolio: Responds to increasing demands on the mathematical sciences by attempting to systematically advance fundamental knowledge and underlying models that focus on the needs of the Army. The areas of focus include computation, complex systems, computer graphics and visualization, geometry, cooperative systems, robotics, discrete mathematics, information networks, probability, simulations, statistics, and stochastic modeling. For example, the Modeling of Complex Systems Program concentrates on the development of metrics based on basic principles for complex models and complex and multi-scale phenomena of interest to the Army. The Discrete Mathematics Program focuses on visualization of high-dimensional and multiscaled data.