AI Based Digital Twin Interoporability Schema

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

Not reported

Withheld from public reporting

Yes (disclosure risk)Source: b) Yes – agency has determined that there’s a risk to disclosure, such as a harm to an interest protected by a FOIA exemption

Mission

Topic area

Science

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Using IEEE 2874 Spatial Web standard to implement a new Hyperspace Modeling Language (HSML) and Hyperspace Transaction Protocol (HSTP) to establish communications among heterogeneous simulation platforms.

Expected benefits

Making collaboration of digital twins possible in different platforms.

System outputs

Recommandation list.

Technology

AI classification

Reinforcement learningSource: Reinforcement Learning: AI trained through trial and error using rewards and penalties to optimize decision-making policies.

System name(s)

Not reported

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

No

Demographic features

Not reported

Training and evaluation data

Omniverse and Unity digital twin objects data are uesed.

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