Quantification of Uncertainty Analysis Toolkit (QUAnT)

Overview

Development stage

PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

2025-06-01 (day precision)Source: 06/01/2025 00:00:00

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Science

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

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.

System outputs

Predictions, decisions

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

MATLAB

Custom-developed code

Yes

Public source code

Not reported

Sourcing

How it was built

Developed in-houseSource: b) Developed in-house

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

No

Privacy Impact Assessment

Not reported

Authorization to Operate

Yes

Demographic features

Not reported

Training and evaluation data

Mission-specific data (thermal, structural, optical, etc.)

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported