Research concentrates on understanding and exploiting the fundamental aspects of large-scale multidimensional data analytics in complex environments. Experiments, observations, and numerical simulations are on the verge of generating petabytes of diverse data at ever increasing rates. These sources of data are distributed over disparate locations on a heterogeneous collection of platforms and pose a challenge in providing real-time analytics in situational understanding, information processing and uncertainty characterization and quantification.
Uncertainty Quantification (APG)
The Army has witnessed a remarkable rise in the complexity of the battlefield. In response, Army systems are rapidly becoming more complex. This added complexity comes at a time when the Army must function under tighter time and resource constraints. In order to account for the complexity, and still be able to satisfy the above constraints, robustness in design, analysis, and decision making becomes absolutely crucial. Since modeling has now become a foundation of design, analysis and decision making, it is critical that robustness and the uncertainties introduced through real-world variability are incorporated into models.
ARL is seeking collaboration opportunities in novel and efficient concepts and stochastic methodologies for high-fidelity assessment on the level of agreement in sets of models relative to input and output data, variations in interdependent models due to various physics, mathematical, and numerical assumptions (enabling. tools to (i) identify deficiencies in simulations; (ii) set guidelines for adequacy of computational results; (iii) explore the impact of known variability and uncertainty of input; and (iv) control of adaptive algorithms to achieve specified levels of accuracy to aid decisions from design to operational planning).
Dr. Ernest Chin, email@example.com, (410) 306-1988