Analyzing large data is the process of methodically and systematically making decisions to reduce incomprehensible datasets down to a manageable size that can be viewed and understood easily. These reductions are made by performing data analytics, producing metrics, identifying patterns, and/or producing any other criteria of comparison that can be mathematically modeled.

The motivation for this work is to mitigate shock levels on vehicle components, through testing new designs of equipment. The objective is to generate a time history to perform shock testing on military and commercial ground vehicle components. The time histories are used to drive hydraulic actuators coupled to a test specimen.

The U.S. Engineer Research and Development Center (ERDC) Military Engineering Program on Remote Assessment of Infrastructure for Ensured Maneuver (RAFTER) Boreal Aspects of Ensured Maneuver (BAEM) identifies the need for modeling over-snow vehicle performance, as many factors related to vehicle setup and land surface condition contribute to vehicle efficiency. Accurately estimating snow macromechanical characteristics—such as elastic modulus, stiffness, and strength—is critical for understanding how effectively a vehicle will travel over snow-covered terrain.

Increasing the operational efficiency of weapons employed in hostile environments is a high priority of the United States Air Force (USAF). In recent history, the USAF has made a move to smaller and internally stored weapons, especially for fighter aircraft. Maintaining a low radar cross section signature, and thus a low observable air vehicle, is desirable so the aircraft is less detectable by the enemy.

Acoustic waves propagating in the atmosphere may undergo many effects including refraction by temperature and wind velocity gradients, scattering by atmospheric turbulence, absorption by the atmosphere (fluid), diffraction by terrain features, and absorption and reflection by a porous ground. As a result, there may be insonification in acoustic shadow zones, amplitude and phase fluctuations of the propagating sound signals, loss of signal coherence, changes in the interference maxima and minima of the direct ground reflected waves, and multipath effects. Understanding these effects is important for a variety of military applications, such as acoustic source localization and classification, noise propagation in the atmosphere, and the development of new remote sensing techniques of the atmosphere.

Spatiotemporal imaging contains a large class of imaging problems, which involve collecting a sequence of data sets to resolve both the spatial and temporal (or spectral) distributions of some physics quantity. This capability is exploited in numerous different fields such as remote sensing, security surveillance systems, astronomical imaging, and biomedical imaging. One typical example is hyperspectral imaging, which is a powerful technology for remotely inferring the material properties of the objects in a scene of interest. Ultrasonic and thermal imaging are other important examples of spatiotemporal imaging where high spatial resolution is needed for urban planning, military planning, intelligence and disaster monitoring and evaluation.

Fiber lasers have found widespread applications in industrial material processing, scientific research and military systems due to their advantages of easy maintenance, excellent stability, compact size and low cost. One characteristic of fiber lasers, compared to other types of lasers, is that strong light is confined to propagating a long distance in a fiber core that has a very small cross-sectional area. This has the consequence that the nonlinear light interaction with the matter has a very long length. This results in the strength of all nonlinear optical processes being strongly amplified. This means that a conventionally weak, nonlinear optical effect can become significant. Therefore, apart from the practical applications, fiber lasers also constitute an ideal platform for the exploration of various complex nonlinear dynamics.