Quantification of Uncertainty Analysis Toolkit (QUAnT)
Description
The Quantification of Uncertainty Analysis Toolkit (QUAnT) is a digital-twin framework that informs and guides the design process of complex, large-scale, multidisciplinary systems throughout their life cycle, while maximizing resources (e.g., size, weight, power, cost, schedule). QUAnT enables: 1) orders-of-magnitude reductions in computational cost through a multi-fidelity simulation approach and the most efficient sampling techniques, 2) maximization of project resources through optimal allocation and task automation, 3) model predictive capabilities through data-driven learning (digital twins), 4) quantification of uncertainty to the maximum extent possible to efficiently identify risk drivers, 5) reliability analyses for rare events through advanced statistical methods. QUAnT lays its foundations on state-of-the-art methodologies described in peer-reviewed literature and leverages artificial intelligence and machine learning to automate and facilitate several tasks. It has successfully been applied to several engineering problems including flown NASA missions such as the James Webb Space Telescope and the ongoing Mars Sample Return, where it demonstrably brought notable savings in terms of time, cost, technical quality and efficiency.
Detailed example
Predictions, decisions
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
QUAnT is expected to have a strategic, long-term, high payoff especially when used early in a project life cycle. This is advantageous as it can increase system knowledge and inform decisions when the design freedom is higher and cheaper (i.e., well before PDR, when 85% of the project’s total life cycle cost is locked in). The anticipated ROI is tied to QUAnT’s ability to guide the mission development process while cutting down on computational cost, thus bringing notable savings in terms of time, cost and efficiency. Namely, QUAnT yields better (10%-50%) margin estimates, leading to time (<1+ year) and cost (<$200M+) savings; efficiencies in analysis cycles yield time and cost savings also thanks to the elimination of obsolete tasks and the workforce needed to perform them (as an example from a real-life case: 2.5 months, $300K for a 5-person team vs. 2 weeks, $5K for 1 person applying this technology). Finally, QUAnT is mathematically proven to provide the highest-quality results, which ensures having the best information available at hand when making decisions under uncertainty. Note, the ROI estimates provided in here were derived from specific cases and can vary but do represent the correct order of magnitude.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
Mission-specific data (thermal, structural, optical, etc.)