Taking the Guesswork out of Designing Extremely Complex Military Systems

Home / Articles / External / Government


June 19, 2017 | Originally published by Date Line: June 19 on

Computational models and simulations can be enormously helpful when designing complex military systems such as new aerospace vehicles and engines, reducing development costs and times. However, realistic, high-fidelity models require enormous amounts of computing power in order to be able to accommodate all of the different factors that may affect predictive accuracy. To mitigate this computational cost, researchers often use simplified models, but these models contain assumptions, ambiguities, incomplete information, and inputs that vary unpredictably. This problem is exacerbated when these uncertainties interact with each other in a complex system. As a result, engineers typically rely on extensive testing to validate their modeling results—a repetitive process of design, test, verify, re-design, re-test, re-verify that can add years to the development process and significantly increase cost.

DARPA’s Enabling Quantification of Uncertainty in Physical Systems (EQUiPS) program has recently made a number of seminal advances addressing this problem by developing mathematical tools and methods to tackle the challenges associated with large systems of many variables and account for the uncertainty in every step of the modeling and design process.

The program is advancing the field of uncertainty quantification, or UQ, which focuses on methods to estimate how accurate a prediction may be. With advanced UQ tools, designers can better understand the risks involved in pursuing certain designs. With that information in hand, the chances increase that new designs for complex military vehicles, vessels, air- and spacecraft will perform as anticipated when a prototype is first built and tested.